U.S. patent application number 12/906036 was filed with the patent office on 2011-04-21 for systems for mobile image capture and remittance processing.
This patent application is currently assigned to MITEK SYSTEMS. Invention is credited to James DeBello, Grigori Nepomniachtchi, Josh Roach.
Application Number | 20110091092 12/906036 |
Document ID | / |
Family ID | 45939032 |
Filed Date | 2011-04-21 |
United States Patent
Application |
20110091092 |
Kind Code |
A1 |
Nepomniachtchi; Grigori ; et
al. |
April 21, 2011 |
SYSTEMS FOR MOBILE IMAGE CAPTURE AND REMITTANCE PROCESSING
Abstract
The present invention relates to automated document processing
and more particularly, to methods and systems for document image
capture and processing using mobile devices. In accordance with
various embodiments, methods and systems for document image capture
on a mobile communication device are provided such that the image
is optimized and enhanced for data extraction from the document as
depicted. These methods and systems may comprise capturing an image
of a document using a mobile communication device; transmitting the
image to a server; and processing the image to create a bi-tonal
image of the document for data extraction. Additionally, these
methods and systems may comprise capturing a first image of a
document using the mobile communication device; automatically
detecting the document within the image; geometrically correcting
the image; binarizing the image; correcting the orientation of the
image; correcting the size of the image; and outputting the
resulting image of the document.
Inventors: |
Nepomniachtchi; Grigori;
(San Diego, CA) ; DeBello; James; (San Diego,
CA) ; Roach; Josh; (San Diego, CA) |
Assignee: |
MITEK SYSTEMS
San Diego
CA
|
Family ID: |
45939032 |
Appl. No.: |
12/906036 |
Filed: |
October 15, 2010 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
12778943 |
May 12, 2010 |
|
|
|
12906036 |
|
|
|
|
12346026 |
Dec 30, 2008 |
|
|
|
12778943 |
|
|
|
|
61022279 |
Jan 18, 2008 |
|
|
|
Current U.S.
Class: |
382/139 ;
382/137 |
Current CPC
Class: |
H04N 2201/0084 20130101;
G06K 9/00463 20130101; H04N 1/00244 20130101; G06T 5/006 20130101;
H04N 2201/001 20130101; G06K 9/38 20130101; G06Q 20/3276 20130101;
H04N 2101/00 20130101; G06K 2209/01 20130101; G06K 9/42 20130101;
G06Q 20/387 20130101; G06K 9/3275 20130101; G06F 40/186 20200101;
H04N 1/00307 20130101; G06Q 20/042 20130101 |
Class at
Publication: |
382/139 ;
382/137 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A computer implemented method for mobile image capture and
remittance processing, where one or more processors are programmed
to perform steps comprising: receiving a mobile image of a
remittance coupon captured using a mobile device; detecting the
remittance coupon within the mobile image of the remittance coupon;
generating a document subimage that includes a portion of the
mobile image that corresponds to the remittance coupon;
geometrically correcting the mobile document image of the
remittance coupon to generate a geometrically corrected image;
processing the geometrically corrected image to generate a
processed image; executing the one or more mobile image quality
assurance tests on the processed image to assess the quality of the
processed image; and executing one or more remittance processing
steps on the processed image if the processed image passes the
mobile image quality assurance tests.
2. The method of claim 1, wherein processing the geometrically
corrected to generate a processed image further comprises:
performing a dewarping operation to correct warping in the
geometrically corrected image.
3. The method of claim 1, wherein processing the geometrically
corrected image to generate a processed image further comprises:
performing a binarization operation on the geometrically corrected
image to generate a bitonal image of the remittance coupon.
4. The method of claim 3, wherein processing the geometrically
corrected image to generate a processed image further comprises:
identifying a code line from the remittance coupon in the bitonal
image of the remittance coupon.
5. The method of claim 4, wherein processing the geometrically
corrected image to generate a processed image further comprises:
determining a size for the code line from the bitonal image of the
remittance coupon to determine a scaling factor.
6. The method of claim 5, wherein processing the geometrically
corrected image to generate a processed image further comprises:
performing a second geometric correction operation on the mobile
document subimage using the scaling factor to generate a scaled
geometrically corrected image.
7. The method of claim 5, wherein performing a second geometric
correction operation on the mobile document subimage further
comprises: identifying an orientation of the remittance coupon in
the mobile document subimage; and correcting the orientation of the
mobile document subimage to be in a landscape orientation if the
remittance coupon was not in a landscape orientation.
8. The method of claim 6, further comprising: identifying an
orientation of the remittance coupon in the mobile document
subimage.
9. The method of claim 7, further comprising: performing a
binarization operation on the scaled geometrically corrected image
to generate a second bitonal image of the remittance coupon.
10. The method of claim 1, wherein executing one or more remittance
processing steps on the processed image if the processed image
passes the mobile image quality assurance tests further comprises:
extracting text content from the processed image and identifying
data in the extracted text content using a remittance coupon
template.
11. The method of claim 10, wherein extracting text content from
the processed image using a remittance coupon template further
comprises: selecting a remittance coupon template from a plurality
of remittance templates in a remittance coupon template data store;
and extracting text content from the processed image using an
optical character recognition technique at locations specified by
the remittance coupon template.
12. The method of claim 11, wherein selecting a remittance coupon
template from a plurality of remittance templates in a remittance
coupon template data store further comprises: analyzing the
processed image using an optical character recognition technique to
identify text content in the processed image; and selecting a
remittance coupon template from a plurality of remittance templates
based on text content identified in the image.
13. The method of claim 11, wherein selecting a remittance coupon
template from a plurality of remittance templates in a remittance
coupon template data store further comprises: analyzing the
processed image to identify a set of landmarks on the remittance
coupon; and selecting a remittance coupon template from a plurality
of remittance templates based on the identified landmarks.
14. The method of claim 1, wherein executing one or more remittance
processing steps on the processed image if the processed image
passes the mobile image quality assurance tests further comprises:
extracting text content from the processed image and identifying
data in the extracted text content using dynamic data capture.
15. The method of claim 14, wherein extracting text content from
the processed image and identifying the extracted content using
dynamic data capture further comprises: locating data fields in the
processed image using optical character recognition; extracting
data from data fields; and analyzing the extracted data to identify
the extracted data.
16. The method of claim 15, wherein locating data fields in the
processed image using optical character recognition further
comprises: matching a set of keywords to text content identified in
the processed image, wherein the set of keywords is associated with
a particular type of data found on a remittance coupon; locating
text proximate to a keyword identified in the processed image; and
associating the text proximate to the keyword with the type of data
associated with the keyword.
17. The method of claim 15, wherein locating data fields in the
processed image using optical character recognition further
comprises: matching a format associated with a particular type of
data found on a remittance coupon to a format of text content
identified in the processed image; and associating the matched text
with the type of data associated with the format.
18. The method of claim 18, wherein locating data fields in the
processed image using optical character recognition further
comprises: identifying a code line in the processed image of the
remittance coupon; and parsing data from the code line and
associating the parsed data with a data type.
19. The method of claim 16, wherein parsing the parsing data from
the code line further comprises: matching a portion of the code
line to other text content extracted from the processed image of
the remittance coupon to identify the portion of the code line.
20. The method of claim 15, wherein executing one or more
remittance processing steps on the processed image if the processed
image passes the mobile image quality assurance tests further
comprises: receiving a second mobile document image of the
remittance coupon from the mobile device; geometrically correcting
the second mobile document image of the remittance coupon to
generate a third geometrically corrected image using the processor
of the mobile device; processing the third geometrically corrected
image at the server to generate a second processed image; and
locating data fields in the second processed image using optical
character recognition; comparing the data fields located in the
second processed image to the data fields from the first processed
image; and selecting values for data fields that match in the first
processed image and the second processed image.
21. A system for mobile image capture and remittance processing,
comprising: an image correction module configured to receive a
mobile image a mobile image of a remittance coupon captured using a
mobile device; detect the remittance coupon within the mobile image
of the remittance coupon; generating a document subimage that
includes a portion of the mobile image that corresponds to the
remittance coupon; geometrically correct the mobile document image
of the remittance coupon to generate a geometrically corrected
image; process the geometrically corrected image at the server to
generate a processed image; a test execution module configured to
execute the one or more mobile image quality assurance tests on the
processed image to assess the quality of the processed image and to
provide the processed image to the remittance processing module if
the processed image passes the mobile image quality assurance
tests; and a remittance processing module configured to execute one
or more remittance processing steps on the processed image.
22. The system of claim 21 wherein the image correction module
being configured to process the geometrically corrected image at
the server to generate a processed image is further configured to:
perform a dewarping operation to correct warping in the
geometrically corrected image.
23. The system of claim 21 wherein the image correction module
being configured to process the geometrically corrected image at
the server to generate a processed image is further configured to:
perform a binarization operation on the geometrically corrected
image to generate a bitonal image of the remittance coupon.
24. The system of claim 23 wherein the image correction module
being configured to process the geometrically corrected image at
the server to generate a processed image is further configured to:
identify a code line of the remittance coupon in the bitonal image
of the remittance coupon.
25. The system of claim 24 wherein the image correction module
being configured to process the geometrically corrected image at
the server to generate a processed image is further configured to:
determine a size for the code line from the bitonal image of the
remittance coupon to determine a scaling factor.
26. The system of claim 25 wherein the image correction module
being configured to process the geometrically corrected image at
the server to generate a processed image is further configured to:
perform a second geometric correction operation on the mobile
document subimage using the scaling factor to generate a scaled
geometrically corrected image.
27. The system of claim 25 wherein the image correction module
being configured to process the geometrically corrected image at
the server to generate a processed image is further configured to:
identify an orientation of the remittance coupon in the mobile
document subimage; and correct the orientation of the mobile
document subimage to be in a landscape orientation if the
remittance coupon was not in a landscape orientation.
28. The system of claim 26 wherein the image correction module is
further configured to: identify an orientation of the remittance
coupon in the mobile document subimage.
29. The system of claim 27 wherein the image correction module is
further configured to: perform a binarization operation on the
scaled geometrically corrected image to generate a second bitonal
image of the remittance coupon.
30. The system of claim 21, wherein the remittance processing
module is configured to: extract text content from the processed
image and identifying data in the extracted text content using a
remittance coupon template.
31. The system of claim 30, wherein the remittance processing
module being configured to extract text content from the processed
image using a remittance coupon template is further configured to:
select a remittance coupon template from a plurality of remittance
templates in a remittance coupon template data store; and extract
text content from the processed image using an optical character
recognition technique at locations specified by the remittance
coupon template.
32. The system of claim 31, wherein the remittance processing
module being configured to select a remittance coupon template from
a plurality of remittance templates in a remittance coupon template
data store further is further configured to: analyze the processed
image using an optical character recognition technique to identify
text content in the processed image; and select a remittance coupon
template from a plurality of remittance templates based on text
content identified in the image.
33. The system of claim 31, wherein the remittance processing
module being configured to select a remittance coupon template from
a plurality of remittance templates in a remittance coupon template
data store further is further configured to: analyze the processed
image to identify a set of landmarks on the remittance coupon; and
select a remittance coupon template from a plurality of remittance
templates based on the identified landmarks.
34. The system of claim 21 wherein the remittance processing module
being configured to executing one or more remittance processing
steps on the processed image is further configured to: extract text
content from the processed image and identify data in the extracted
text content using dynamic data capture.
35. The system of claim 34 wherein the remittance processing module
being configured to extract text content from the processed image
and identify the extracted content using dynamic data capture is
further configured to: locate data fields in the processed image
using optical character recognition; extract data from data fields;
and analyze the extracted data to identify the extracted data.
36. The system of claim 35 wherein the remittance processing module
being configured to locate data fields in the processed image using
optical character recognition is further configured to: match a set
of keywords to text content identified in the processed image,
wherein the set of keywords is associated with a particular type of
data found on a remittance coupon; locate text proximate to a
keyword identified in the processed image; and associate the text
proximate to the keyword with the type of data associated with the
keyword.
37. The system of claim 35 wherein the remittance processing module
being configured to locate data fields in the processed image using
optical character recognition is further configured to: match a
format associated with a particular type of data found on a
remittance coupon to a format of text content identified in the
processed image; and associate the matched text with the type of
data associated with the format.
38. The system of claim 35 wherein the remittance processing module
being configured to locate data fields in the processed image using
optical character recognition is further configured to: identify a
code line in the processed image of the remittance coupon; and
parse data from the code line and associating the parsed data with
a data type.
39. The system of claim 38 wherein the remittance processing module
being configured to parse the parsing data from the code line is
further configured to: match a portion of the code line to other
text content extracted from the processed image of the remittance
coupon to identify the portion of the code line.
40. The system of claim 35 wherein the remittance processing module
being configured to executing one or more remittance processing
steps on the processed image is further configured to: receiving a
second mobile document image of the remittance coupon from the
mobile device; geometrically correcting the second mobile document
image of the remittance coupon to generate a third geometrically
corrected image using the processor of the mobile device;
processing the third geometrically corrected image at the server to
generate a second processed image; and locating data fields in the
second processed image using optical character recognition;
comparing the data fields located in the second processed image to
the data fields from the first processed image; and selecting
values for data fields that match in the first processed image and
the second processed image.
Description
RELATED APPLICATIONS INFORMATION
[0001] This application is a continuation in part of copending U.S.
patent application Ser. No. 12/778,943 filed on May 12, 2010, and
is also a continuation in part of U.S. patent application Ser. No.
12/346,026 filed Dec. 30, 2008, which in turn claims the benefit of
U.S. Provisional application Ser. No. 61/022,279 filed Jan. 18,
2008, all of which are incorporated herein by reference in their
entirety as if set forth in full. This application is also related
to U.S. patent application Ser. No. 12/717,080 filed Mar. 3, 2010,
which is now U.S. Pat. No. 7,778,457, which is incorporated herein
by reference in its entirety as if set forth in full.
BACKGROUND
[0002] 1. Technical Field
[0003] The embodiments described herein generally relate to
automated document processing and more particularly, to systems and
methods for document image processing that enhances an image for
data extraction from images captured on a mobile device with camera
capabilities.
[0004] 2. Related Art
[0005] Remittance processing services provide payment processing
services for other businesses. A remittance processing service may
be set up by a bank. Often these services set up one or more post
office boxes for receiving payments from customers in the form of
paper checks. Typically the user will include a remittance slip or
remittance coupon with the payment.
[0006] Banks and other businesses often provide remittance slips or
coupons that a customer can include with a payment. These coupons
may be included with an invoice or provided to a customer in
advance. For example, some mortgage lenders or automobile lenders
provide a book of remittance coupons to customers who then tear out
a mail a remittance coupon with each scheduled payment. The
remittance coupons generally include customer account information,
an amount due, and a due date for the payment. The customer account
information might include an account holder name, mailing address,
and a customer account number. Other information, such as the
mailing address of the bank or business may also be included on the
remittance coupon. Some remittance coupons also include
computer-readable bar codes or code lines that include text or
other computer-readable symbols that can be used to encode
account-related information that can be used to reconcile a payment
received with the account for which the payment is being made. The
bar code or code line can be detected and decoded by a computer
system to extract the information encoded therein.
[0007] In general, the remittance coupon is mailed with a check or
other form of payment to the bank or other business or to a payment
processing company designated by the bank or other business. In
some systems, data entry clerks may manually enter payment
information into a payment processing system. In other systems, the
remittance coupon and/or the check may be scanned by a computer
system for automated processing.
SUMMARY
[0008] Systems and method are provided for capturing and processing
images of remittance coupons using a mobile device, such as a
mobile phone. Embodiments of the systems and methods described
herein provide image optimization and enhancement such that data
can be extracted from the remittance coupon for processing by a
remittance processing service. In some embodiments, an image of a
check can also be captured to be processed as a payment associated
with the remittance coupon. Some embodiments described herein
involve a mobile communication device capturing an image of a
document and transmitting the captured image to a server for image
optimization and enhancement. Techniques for assessing the quality
of images of documents captured using the mobile device are also
provided. The tests can be selected based on the type of document
that was imaged, the type of mobile application for which the image
quality of the mobile image is being assessed, and/or other
parameters such as the type of mobile device and/or the
characteristics of the camera of the mobile device that was used to
capture the image. In some embodiments, the image quality assurance
techniques can be implemented on a remote server, such as a mobile
phone carrier's server or a web server, and the mobile device
routes the mobile image to be assessed and optional processing
parameters to the remote server processing and the test results can
be passed from the remote server to the mobile device.
[0009] According to an embodiment, a computer implemented method
for mobile image capture and remittance processing is provided
where one or more processors are programmed to perform steps of the
method. The steps of the method include receiving a mobile image of
a remittance coupon captured using a mobile device, detecting the
remittance coupon within the mobile image of the remittance coupon,
generating a document subimage that includes a portion of the
mobile image that corresponds to the remittance coupon,
geometrically correcting the mobile document image of the
remittance coupon to generate a geometrically corrected image,
processing the geometrically corrected image to generate a
processed image, executing the one or more mobile image quality
assurance tests on the processed image to assess the quality of the
processed image; and executing one or more remittance processing
steps on the processed image if the processed image passes the
mobile image quality assurance tests.
[0010] According to another embodiment, a system for mobile image
capture and remittance processing is provided. The system includes
a mobile device that is configured to capture a mobile image of a
remittance coupon using a mobile device, detect the remittance
coupon within the mobile image of the remittance coupon using a
processor of the mobile device and generating a document subimage
that includes a portion of the mobile image that corresponds to the
remittance coupon, and transmit the mobile document subimage to a
server. The system also includes a mobile remittance server that
includes an image correction module, a test execution module, and a
remittance processing module. The image correction module is
configured to geometrically correct the mobile document image of
the remittance coupon to generate a geometrically corrected image,
process the geometrically corrected image at the server to generate
a processed image. The test execution module is configured to
execute the one or more mobile image quality assurance tests on the
processed image to assess the quality of the processed image and to
provide the processed image to the remittance processing module if
the processed image passes the mobile image quality assurance
tests. The remittance processing module is configured to execute
one or more remittance processing steps on the processed image.
[0011] Other features and advantages of the present invention
should become apparent from the following description of the
preferred embodiments, taken in conjunction with the accompanying
drawings, which illustrate, by way of example, the principles of
the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The various embodiments provided herein are described in
detail with reference to the following figures. The drawings are
provided for purposes of illustration only and merely depict
typical or example embodiments. These drawings are provided to
facilitate the reader's understanding of the invention and shall
not be considered limiting of the breadth, scope, or applicability
of the embodiments. It should be noted that for clarity and ease of
illustration these drawings are not necessarily made to scale.
[0013] FIG. 1 is an image of a remittance coupon 100 illustrating
an example remittance coupon that might be imaged with the systems
and methods described herein.
[0014] FIG. 2 is a geometrically corrected image created using
image processing techniques disclosed herein using the mobile image
of the remittance coupon 100 illustrated in FIG. 1.
[0015] FIG. 3 is high level block diagram of a system that can be
used to implement the systems and methods described herein.
[0016] FIG. 4 is a flow diagram of an example method for capturing
an image of a remittance coupon using a mobile device according to
an embodiment.
[0017] FIG. 5 is a flow diagram of an example method for processing
an image of a remittance coupon captured using a mobile device
according to an embodiment.
[0018] FIG. 6 is a flow diagram illustrating a method for
correcting defects to mobile image according to an embodiment.
[0019] FIG. 7 and its related description above provide some
examples of how a perspective transformation can be constructed for
a quadrangle defined by the corners A, B, C, and D according to an
embodiment.
[0020] FIG. 8 is a diagram illustrating an example original image,
focus rectangle and document quadrangle ABCD in accordance with the
example of FIG. 7.
[0021] FIG. 9 is a flow chart for a method that can be used to
identify the corners of the remittance coupon in a color image
according to an embodiment.
[0022] FIG. 10 is a flow diagram of a method for generating a
bi-tonal image according to an embodiment.
[0023] FIG. 11 illustrates an example image binarized image of a
remittance coupon generated from the remittance geometrically
corrected remittance coupon image illustrated in FIG. 2.
[0024] FIG. 12 is a flow diagram of a method for converting a
document image into a smaller color icon image according to an
embodiment.
[0025] FIG. 13A is a mobile image of a check according to an
embodiment.
[0026] FIG. 13B is an example of a color icon image generated using
the method of FIG. 12 on the example mobile image of a check
illustrated in FIG. 13A according to an embodiment.
[0027] FIG. 14 is a flow diagram of a method for reducing the color
depth of an image according to an embodiment.
[0028] FIG. 15A depicts an example of the color "icon" image of
FIG. 13B after operation 1302 has divided it into a 3.times.3 grid
in accordance with one embodiment of the invention.
[0029] FIG. 15B depicts an example of the color "icon" image of
FIG. 13B converted to a gray "icon" image using the method
illustrated in FIG. 14 according to an embodiment.
[0030] FIG. 16 is a flowchart illustrating an example method for
finding document corners from a gray "icon" image containing a
document according to an embodiment.
[0031] FIG. 17 is a flowchart that illustrates an example method
for geometric correction according to an embodiment.
[0032] FIG. 18A is an image illustrating a mobile image of a check
that is oriented in landscape orientation according to an
embodiment.
[0033] FIG. 18B example gray-scale image of the document depicted
in FIG. 13A once a geometrical correction operation has been
applied to the image according to an embodiment.
[0034] FIG. 19 is a flow chart illustrating a method for correcting
landscape orientation of a document image according to an
embodiment.
[0035] FIG. 20 provides a flowchart illustrating an example method
for size correction of an image according to an embodiment.
[0036] FIG. 21 illustrates a mobile document image processing
engine (MDIPE) module 2100 for performing quality assurance testing
on mobile document images according to an embodiment.
[0037] FIG. 22 is a flow diagram of a process for performing mobile
image quality assurance on an image captured by a mobile device
according to an embodiment.
[0038] FIG. 23 is a flow diagram of a process for performing mobile
image quality assurance on an image of a check captured by a mobile
device according to an embodiment.
[0039] FIG. 24A illustrates a mobile image where the document
captured in the mobile document image exhibits view distortion.
[0040] FIG. 24B illustrates an example of a grayscale geometrically
corrected subimage generated from the distorted image in FIG. 24A
according to an embodiment.
[0041] FIG. 25A illustrates an example of an in-focus mobile
document image.
[0042] FIG. 25B illustrates an example of an out of focus
document.
[0043] FIG. 26 illustrates an example of a shadowed document.
[0044] FIG. 27 illustrates an example of a grayscale snippet
generated from a mobile document image of a check where the
contrast of the image is very low according to an embodiment.
[0045] FIG. 28 illustrates a method for executing a Contrast IQA
Test according to an embodiment.
[0046] FIG. 29A is an example of a mobile document image that
includes a check that exhibits significant planar skew according to
an embodiment.
[0047] FIG. 29B illustrates an example of a document subimage that
exhibits view skew according to an embodiment.
[0048] FIG. 30 is a flow chart illustrating a method for testing
for view skew according to an embodiment.
[0049] FIG. 31 illustrates an example of a mobile document image
that features an image of a document where one of the corners of
the document has been cut off in the picture.
[0050] FIG. 32 illustrates a Cut-Off Corner Test that can be used
for testing whether corners of a document in a document subimage
have been cut off when the document was imaged according to an
embodiment.
[0051] FIG. 33 illustrates an example of a mobile document image
that features a document where one of the ends of the document has
been cut off in the image.
[0052] FIG. 34 is a flow diagram of a method for determining
whether one or more sides of the document are cut off in the
document subimage according to an embodiment.
[0053] FIG. 35 illustrates an example of a mobile document image
where the document is warped according to an embodiment.
[0054] FIG. 36 is a flow diagram of a method for identifying a
warped image and for scoring the image based on how badly the
document subimage is warped according to an embodiment.
[0055] FIG. 37 illustrates an example of a document subimage within
a mobile document image that is relatively small in comparison to
the overall size of the mobile document image according to an
embodiment.
[0056] FIG. 38 is a flow diagram of a process that for performing
an Image Size Test on a subimage according to an embodiment.
[0057] FIG. 39A is a flow chart of a method for executing a
MICR-line Test according to an embodiment.
[0058] FIG. 39B is a flow chart of a method for executing a code
line Test according to an embodiment.
[0059] FIG. 40 illustrates a method for executing an Aspect Ratio
Test according to an embodiment.
[0060] FIG. 41 illustrates a method for performing a front-as-rear
test on a mobile document image.
[0061] FIG. 42 is a flow chart of a method for processing an image
of a remittance coupon using form identification according to an
embodiment.
[0062] FIG. 43 is a flow chart of a method for processing an image
of a remittance coupon using form identification according to an
embodiment.
[0063] FIG. 44 is a block diagram of various functional elements of
a mobile device that can be used with the various systems and
methods described herein according to an embodiment.
[0064] FIG. 45 is a block diagram of functional elements of a
computer system that can be used to implement the mobile device
and/or the servers described in the systems and methods disclosed
herein.
[0065] FIG. 46A-46F are images representing application that can
reside on mobile device and be using in according with the systems
and method disclosed herein according to an embodiment.
DETAILED DESCRIPTION
[0066] The embodiments described herein are directed towards
automated document processing and systems and methods for document
image processing using mobile devices. System and methods are
provided for processing a remittance coupon captured using a mobile
device. Generally, in some embodiments an original color image of a
document is captured using a mobile device and then converted the
color image to a bi-tonal image. More specifically, in some
embodiments, a color image of a document taken by a mobile device
is received and converted into a bi-tonal image of the document
that is substantially equivalent in its resolution, size, and
quality to document images produced by "standard" scanners.
[0067] The term "standard scanners" as used herein, but is not
limited to, transport scanners, flat-bed scanners, and specialized
check-scanners. Some manufacturers of transport scanners include
UNISYS.RTM., BancTec.RTM., IBM.RTM., and Canon.RTM.. With respect
to specialized check-scanners, some models include the
TellerScan.RTM. TS200 and the Panini.RTM. My Vision X. Generally,
standard scanners have the ability to scan and produce high quality
images, support resolutions from 200 dots per inch to 300 dots per
inch (DPI), produce gray-scale and bi-tonal images, and crop an
image of a check from a larger full-page size image. Standard
scanners for other types of documents may have similar capabilities
with even higher resolutions and higher color-depth.
[0068] The term "color images" as used herein, but is not limited
to, images having a color depth of 24 bits per a pixel (24
bit/pixel), thereby providing each pixel with one of 16 million
possible colors. Each color image is represented by pixels and the
dimensions W (width in pixels) and H (height in pixels). An
intensity function I maps each pixel in the [W.times.H] area to its
RGB-value. The RGB-value is a triple (R,G,B) that determines the
color the pixel represents. Within the triple, each of the R(Red),
G(Green) and B(Blue) values are integers between 0 and 255 that
determine each respective color's intensity for the pixel.
[0069] The term, "gray-scale images" as used herein, but is not
limited to, images having a color depth of 8 bits per a pixel (8
bit/pixel), thereby providing each pixel with one of 256 shades of
gray. As a person of ordinary skill in the art would appreciate,
gray-scale images also include images with color depths of other
various bit levels (e.g. 4 bit/pixel or 2 bit/pixel). Each
gray-scale image is represented by pixels and the dimensions W
(width in pixels) and H (height in pixels). An intensity function I
maps each pixel in the [W.times.H] area onto a range of gray
shades. More specifically, each pixel has a value between 0 and 255
which determines that pixel's shade of gray.
[0070] Bi-tonal images are similar to gray-scale images in that
they are represented by pixels and the dimensions W (width in
pixels) and H (height in pixels). However, each pixel within a
bi-tonal image has one of two colors: black or white. Accordingly,
a bi-tonal image has a color depth of 1 bit per a pixel (1
bit/pixel). The similarity transformation, as utilized by some
embodiments of the invention, is based off the assumption that
there are two images of [W.times.H] and [W'.times.H'] dimensions,
respectively, and that the dimensions are proportional (i.e.
W/W'=H/H'). The term "similarity transformation" may refer to a
transformation ST from [W.times.H] area onto [W'.times.H'] area
such that ST maps pixel p=p(x,y) on pixel p'=p'(x',y') with x'=x*
W'/W and y=y*H'/H.
[0071] The systems and methods provided herein advantageously allow
a user to capture an image of a remittance coupon, and in some
embodiments, a form of payment, such as a check, for automated
processing. Typically, a remittance processing service will scan
remittance coupons and checks using standard scanners that provide
a clear image of the remittance coupon and accompanying check.
Often these scanners produce either gray-scale and bi-tonal images
that are then used to electronically process the payment. The
systems and methods disclosed herein allow an image of remittance
coupons, and in some embodiments, checks to be captured using a
camera or other imaging device included in or coupled to a mobile
device, such as a mobile phone. The systems and methods disclosed
herein can test the quality of a mobile image of a document
captured using a mobile device, correct some defects in the image,
and convert the image to a format that can be processed by
remittance processing service.
[0072] FIG. 1 is an image illustrating an example remittance coupon
1900 that can be imaged with the systems and methods described
herein. The mobile image capture and processing systems and methods
described herein can be used with a variety of documents, including
financial documents such as personal checks, business checks,
cashier's checks, certified checks, and warrants. By using an image
of the remittance coupon 100, the remittance process can be
automated and performed more efficiently. As would be appreciated
by those of skill in the art, remittance coupons are not the only
types of documents that might be processed using the system and
methods described herein. For example, in some embodiments, a user
can capture an image of a remittance coupon and an image of a check
associated with a checking account from which the remittance
payment will be funded.
[0073] FIG. 2 is a geometrically corrected image created using
image processing techniques disclosed herein and using the mobile
image of the remittance coupon 100 illustrated in FIG. 1. A
remittance coupon may include various fields, and some fields in
the documents might be considered "primary" fields. For example,
some remittance coupons also include computer-readable bar codes or
code lines 205 that include text or other computer-readable symbols
that can be used to encode account-related information. The
account-related information can be used to reconcile a payment
received with the account for which the payment is being made. Code
line 205 can be detected and decoded by a computer system to
extract the information encoded therein. The remittance coupon can
also include an account number field 210 and an amount due field
215. Remittance coupons can also include other fields, such as the
billing company name and address 220, a total outstanding balance,
a minimum payment amount, a billing date, and payment due date. The
examples are merely illustrative of the types of information that
may be included on a remittance coupon and it will be understood
that other types of information can be included on other types of
remittance coupons.
[0074] FIG. 3 is high level block diagram of a system 300 that can
be used to implement the systems and methods described herein.
System 300 include a mobile device 340. The mobile device can
comprise a mobile telephone handset, Personal Digital Assistant, or
other mobile communication device. The mobile device can include a
camera or other imaging device, such as a scanner, or might include
functionality that allows it to connect to a camera or other
imaging device. The connection to an external camera or other
imaging device can comprise a wired or wireless connection. In this
way the mobile device can connect to an external camera or other
imaging device and receive images from the camera or other imaging
device.
[0075] Images of the documents taken using the mobile device or
downloaded to the mobile device can be transmitted to mobile
remittance server 310 via network 330. Network 330 can comprise one
or more wireless and/or wired network connections. For example, in
some cases, the images can be transmitted over a mobile
communication device network, such as a code division multiple
access ("CDMA") telephone network, or other mobile telephone
network. Network 330 can also comprise one or more connections
across the Internet. Images taken using, for example, a mobile
device's camera, can be 24 bit per pixel (24 bit/pixel) JPG images.
It will be understood, however, that many other types of images
might also be taken using different cameras, mobile devices,
etc.
[0076] Mobile remittance server 310 can be configured to perform
various image processing techniques on images of remittance
coupons, checks, or other financial documents captured by the
mobile device 340. Mobile remittance server 310 can also be
configured to perform various image quality assurance tests on
images of remittance coupons or financial documents captured by the
mobile device 340 to ensure that the quality of the captured images
is sufficient to enable remittance processing to be performed using
the images. Examples of various processing techniques and testing
techniques that can be implemented on mobile remit server 210 are
described in detail below.
[0077] Mobile remittance server 310 can also be configured to
communicate with one or more remittance processor servers 315.
According to an embodiment, the mobile remittance server 310 can
perform processing and testing on images captured by mobile device
340 to prepare the images for processing by a third-party
remittance processor and to ensure that the images are of a
sufficient quality for the third-party remittance processor to
process. The mobile remittance server 310 can send the processed
images to the remittance processor 315 via the network 330. In some
embodiments, the mobile remittance server 310 can send additional
processing parameters and data to the remittance processor 315 with
the processed mobile image. This information can include
information collected from a user by the mobile device 340.
According to an embodiment, the mobile remittance server 310 can be
implemented using hardware or a combination of software and
hardware. FIG. 45, describe below, illustrates a computer system
that can be used to implement mobile remittance server 310
according to an embodiment.
[0078] According to an embodiment, the mobile remittance server 310
can be configured to communicate to one or more bank server 320 via
the network 330. Bank server 320 can be configured to process
payments in some embodiments. For example, in some embodiments,
mobile device 340 can be used to capture an image of a remittance
coupon and an image of a check that can be used to make an
electronic payment of the remittance payment. For example, the
remittance processor server 315 can be configured to receive an
image of a remittance coupon and an image of a check from the
mobile remittance server 310. The remittance processor 315 can
electronically deposit the check into a bank account associated
with the entity for which the electronic remittance is being
performed. According to some embodiments, the bank server 320 and
the remittance processor 315 can be implemented on the same server
or same set of servers.
[0079] In other embodiments, the remittance processor 315 can
handle payment. For example, the remittance processor can be
operate by or on behalf of an entity associated with the coupon of
FIG. 1, such as a utility or business. The user's account can then
be linked with a bank, Paypal, or other account, such that when
remittance processor 315 receives the remittance information, it
can charge the appropriate amount to the user's account. FIGS.
46A-F illustrates example screens of an application that can run on
a mobile device 340 and can be used for mobile remittance as
described herein.
[0080] FIG. 46A is an image of an icon representing application
that can reside on mobile device 340. When the application is
activated, a login screen as illustrated in FIG. 46B can be
displayed into which the user can input certain credentials such as
a login or username and password. Once these credentials are
validated, the user can be presented a screen as illustrated in
FIG. 46C allowing them to pay certain bills using the application.
In certain embodiments, the user can also view other information
such as previous or last payments.
[0081] When the user elects to pay a bill, the camera application
can be launched as illustrated in FIG. 46D, which can enable the
user to obtain an image of the coupon. As illustrated, control
buttons can be presented to allow the user to accept the image or
cancel and possibly obtain a new image. Data can then be extracted
from the image, as discussed in detail below, and as illustrated in
FIG. 46E. The data can be presented as various fields and can
enable the user to correct or alter the information as appropriate.
In certain embodiments, the user can even access a grayscale image
of the coupon illustrated in FIG. 46F.
[0082] Once the image is captured and corrected, and the data is
extracted and adjusted, then the image, data, and any required
credential information, such as username, password, and phone or
device identifier, can be transmitted to the mobile remittance
server 310 for further processing. This further processing is
described in detail with respect to the remaining figure sin the
description below.
[0083] First, FIG. 4 is a flow diagram of an example method for
capturing an image of a remittance coupon using a mobile device
according to an embodiment. According to an embodiment, the method
illustrated in FIG. 4 can be implemented in mobile device 340.
[0084] An image of a remittance coupon is captured using a camera
or other optical device of the mobile device 340 (step 405). For
example, a user of the mobile device 340 can click a button or
otherwise activate a camera or other optical device of mobile
device 340 to cause the camera or other optical device to capture
an image of a remittance coupon. FIG. 1 illustrates an example of a
mobile image of a remittance coupon that has been captured using a
camera associated with a mobile device 340.
[0085] According to an embodiment, the mobile device 340 can also
be configured to optionally receive additional information from the
user (step 410). For example, in some embodiments, the mobile
device can be configured to prompt the user to enter data, such as
a payment amount that represents an amount of the payment that the
user wishes to make. The payment amount can differ from the account
balance or minimum payment amount shown on the remittance coupon.
For example, the remittance coupon might show an account balance of
$1000 and a minimum payment amount of $100, but the user might
enter a payment amount of $400.
[0086] According to an embodiment, the mobile device 340 can be
configured to perform some preprocessing on the mobile image (step
415). For example, the mobile device 340 can be configured to
convert the mobile image from a color image to a grayscale image or
to bitonal image. Other preprocessing steps can also be performed
on the mobile device. For example, the mobile device can be
configured to identify the corners of the remittance coupon and to
perform geometric corrections and/or warping corrections to correct
defects in the mobile image. Examples of various types of
preprocessing that can be performed on the mobile device 340 are
described in detail below.
[0087] Mobile device 340 can then transmit the mobile image of the
remittance coupon and any additional data provided by user to
mobile remittance server 310.
[0088] FIG. 5 is a flow diagram of an example method for processing
an image of a remittance coupon captured using a mobile device
according to an embodiment. According to an embodiment, the method
illustrated in FIG. 4 can be implemented in mobile remittance
server 310.
[0089] Mobile remittance server 310 can receive the mobile image
and any data provided by the user from the mobile device 340 via
the network 330 (step 505). The mobile remittance server 310 can
then perform various processing on the image to prepare the image
for image quality assurance testing and for submission to a
remittance processor 315 (step 510). Various processing steps can
be performed by the mobile remittance server 310. Examples of the
types of processing that can be performed by mobile remittance
server 310 are described in detail below.
[0090] Mobile remittance server 310 can perform image quality
assurance testing on the mobile image to determine whether there
are any issues with the quality of the mobile image that might
prevent the remittance provider from being able to process the
image of the remittance coupon (step 515). Various mobile quality
assurance testing techniques that can be performed by mobile
remittance server 310 are described in detail below.
[0091] According to an embodiment, mobile remittance server 310 can
be configured to report the results of the image quality assurance
testing to the mobile device 340 (step 520). This can be useful for
informing a user of mobile device 340 that an image that the user
captured of a remittance coupon passed quality assurance testing,
and thus, should be of sufficient quality that the mobile image can
be processed by a remittance processor server 315. According to an
embodiment, the mobile remittance server 310 can be configured to
provide detailed feedback messages to the mobile device 340 if a
mobile image fails quality assurance testing. Mobile device 340 can
be configured to display this feedback information to a user of the
device to inform the user what problems were found with the mobile
image of the remittance coupon and to provide the user with the
opportunity to retake the image in an attempt to correct the
problems identified.
[0092] If the mobile image passes the image quality assurance
testing, the mobile remittance server 310 can submit the mobile
image plus any processing parameters received from the mobile
device 340 to the remittance processor server 315 for processing
(step 525). According to an embodiment, mobile remittance server
310 can include a remittance processing server configured to
perform the steps 525 including the methods illustrated in FIGS. 42
and 43. According to an embodiment, the remittance processing step
525 can include identifying a type of remittance coupon found in a
mobile image. According to some embodiments, coupon templates can
be used to improve data capture accuracy. According to an
embodiment, if form identification fails and a coupon template that
matches the format of a coupon, a dynamic data capture method can
be applied to extract information from the remittance coupon.
Image Processing
[0093] Mobile device 340 and mobile remittance server 310 can be
configured to perform various processing on a mobile image to
correct various defects in the image quality that could prevent the
remittance processor 215 from being able to process the remittance
due to poor image quality.
[0094] For example, an out of focus image of a remittance coupon or
check, in embodiments where the mobile device can also be used to
capture check images for payment processing, can be impossible to
read an electronically process. For example, optical character
recognition of the contents of the imaged document based on a
blurry mobile image could result in incorrect payment information
being extracted from the document. As a result, the wrong account
could be credited for the payment or an incorrect payment amount
could be credited. This may be especially true if a check and a
payment coupon are both difficult to read or the scan quality is
poor.
[0095] Many different factors may affect the quality of an image
and the ability of a mobile device based image capture and
processing system. Optical defects, such as out-of-focus images (as
discussed above), unequal contrast or brightness, or other optical
defects, can make it difficult to process an image of a document,
e.g., a check, payment coupon, deposit slip, etc. The quality of an
image can also be affected by the document position on a surface
when photographed or the angle at which the document was
photographed. This affects the image quality by causing the
document to appear, for example, right side up, upside down,
skewed, etc. Further, if a document is imaged while upside-down it
might be impossible or nearly impossible to for the system to
determine the information contained on the document.
[0096] In some cases, the type of surface might affect the final
image. For example, if a document is sitting on a rough surface
when an image is taken, that rough surface might show through. In
some cases the surface of the document might be rough because of
the surface below it. Additionally, the rough surface may cause
shadows or other problems that might be picked up by the camera.
These problems might make it difficult or impossible to read the
information contained on the document.
[0097] Lighting may also affect the quality of an image, for
example, the location of a light source and light source
distortions. Using a light source above a document can light the
document in a way that improves the image quality, while a light
source to the side of the document might produce an image that is
more difficult to process. Lighting from the side can, for example,
cause shadows or other lighting distortions. The type of light
might also be a factor, for example, sun, electric bulb, florescent
lighting, etc. If the lighting is too bright, the document can be
washed out in the image. On the other hand, if the lighting is too
dark, it might be difficult to read the image.
[0098] The quality of the image can also be affected by document
features, such as, the type of document, the fonts used, the colors
selected, etc. For example, an image of a white document with black
lettering may be easier to process than a dark colored document
with black letters. Image quality may also be affected by the
mobile device used. Some mobile camera phones, for example, might
have cameras that save an image using a greater number of mega
pixels. Other mobile cameras phones might have an auto-focus
feature, automatic flash, etc. Generally, these features may
improve an image when compared to mobile devices that do not
include such features.
[0099] A document image taken using a mobile device might have one
or more of the defects discussed above. These defects or others may
cause low accuracy when processing the image, for example, when
processing one or more of the fields on a document. Accordingly, in
some embodiments, systems and methods using a mobile device to
create images of documents can include the ability to identify poor
quality images. If the quality of an image is determined to be
poor, a user may be prompted to take another image.
Detecting an Out of Focus Image
[0100] Mobile device 340 and mobile remittance server 310 can be
configured to detect an out of focus image. A variety of metrics
might be used to detect an out-of-focus image. For example, a focus
measure can be employed. The focus measure can be the ratio of the
maximum video gradient between adjacent pixels measured over the
entire image and normalized with respect to an image's gray level
dynamic range and "pixel pitch". The pixel pitch may be the
distance between dots on the image. In some embodiments a focus
score might be used to determine if an image is adequately focused.
If an image is not adequately focused, a user might be prompted to
take another image.
[0101] According to an embodiment, the mobile device 340 can be
configured to detect whether an image is out of focus using the
techniques disclosed herein. In an embodiment, the mobile
remittance server 310 can be configured to detect out of focus
images. In some embodiments, the mobile remittance server 310 can
be configured to detect out of focus images and reject these images
before performing mobile image quality assurance testing on the
image. In other embodiments, detecting and out of focus image can
be part of the mobile image quality assurance testing.
[0102] According to an embodiment, an image focus score can be
calculated as a function of maximum video gradient, gray level
dynamic range and pixel pitch. For example, in one embodiment:
Image Focus Score=(Maximum Video Gradient)*(Gray Level Dynamic
Range)*(Pixel Pitch) (eq. 1)
[0103] The video gradient may be the absolute value of the gray
level for a first pixel "i" minus the gray level for a second pixel
"i+1". For example:
Video Gradient=ABS[(Grey level for pixel "i")-(Gray level for pixel
"i+1")] (eq. 2)
[0104] The gray level dynamic range may be the average of the "n"
lightest pixels minus the average of the "n" darkest pixels. For
example:
Gray Level Dynamic Range=[AVE("N" lightest pixels)-AVE("N" darkest
pixels)] (eq. 3)
[0105] In equation 3 above, N can be defined as the number of
pixels used to determine the average darkest and lightest pixel
gray levels in the image. In some embodiments, N can be chosen to
be 64. Accordingly, in some embodiments, the 64 darkest pixels are
averaged together and the 64 lightest pixels are averaged together
to compute the gray level dynamic range value.
[0106] The pixel pitch can be the reciprocal of the image
resolution, for example, in dots per inch.
Pixel Pitch=[1/Image Resolution] (eq. 4)
[0107] In other words, as defined above, the pixel pitch is the
distance between dots on the image because the Image Resolution is
the reciprocal of the distance between dots on an image.
Detecting and Correcting Perspective Distortion
[0108] FIG. 7 is a diagram illustrating an example of perspective
distortion in an image of a rectangular shaped document. An image
can contain perspective transformation distortions 2500 such that a
rectangle can become a quadrangle ABCD 2502, as illustrated in the
figure. The perspective distortion can occur because an image is
taken using a camera that is placed at an angle to a document
rather than directly above the document. When directly above a
rectangular document it will generally appear to be rectangular. As
the imaging device moves from directly above the surface, the
document distorts until it can no longer be seen and only the edge
of the page can be seen.
[0109] The dotted frame 2504 comprises the image frame obtained by
the camera. The image frame is be sized h.times.w, as illustrated
in the figure. Generally, it can be preferable to contain an entire
document within the h.times.w frame of a single image. It will be
understood, however, that some documents are too large or include
too many pages for this to be preferable or even feasible.
[0110] In some embodiments, an image can be processed, or
preprocessed, to automatically find and "lift" the quadrangle 2502.
In other words, the document that forms quadrangle 502 can be
separated from the rest of the image so that the document alone can
be processed. By separating quadrangle 2502 from any background in
an image, it can then be further processed.
[0111] The quadrangle 2502 can be mapped onto a rectangular bitmap
in order to remove or decrease the perspective distortion.
Additionally, image sharpening can be used to improve the
out-of-focus score of the image. The resolution of the image can
then be increased and the image converted to a black-and-white
image. In some cases, a black-and-white image can have a higher
recognition rate when processed using an automated document
processing system in accordance with the systems and methods
described herein.
[0112] An image that is bi-tonal, e.g., black-and-white, can be
used in some systems. Such systems can require an image that is at
least 200 dots per inch resolution. Accordingly, a color image
taken using a mobile device can need to be high enough quality so
that the image can successfully be converted from, for example, a
24 bit per pixel (24 bit/pixel) RGB image to a bi-tonal image. The
image can be sized as if the document, e.g., check, payment coupon,
etc., was scanned at 200 dots per inch.
[0113] FIG. 8 is a diagram illustrating an example original image,
focus rectangle and document quadrangle ABCD in accordance with the
example of FIG. 7. In some embodiments it can be necessary to place
a document for processing at or near the center of an input image
close to the camera. All points A, B, C and D are located in the
image, and the focus rectangle 2602 is located inside quadrangle
ABCD 2502. The document can also have a low out-of-focus score and
the background surrounding the document can be selected to be
darker than the document. In this way, the lighter document will
stand out from the darker background.
Image Correction Module
[0114] FIG. 6 is a flow diagram illustrating a method for
correcting defects to mobile image according to an embodiment.
According to an embodiment, the method illustrated in FIG. 6 can be
performed by an image correction module implemented on the mobile
remittance server 310. The method illustrated in FIG. 6 can be
implemented as part of step 510 of the method illustrated in FIG.
5. The image correction module can also receive a mobile image an
processing parameters from a mobile device (step 505 of FIG. 5).
According to some embodiments, some or all of the image correction
functionality of the image correction module can be implemented on
the mobile device 340, and the mobile device 340 can be configured
to send a corrected mobile image to the mobile remittance server
310 for further processing.
[0115] According to an embodiment, the image correction module can
also be configured to detect an out of focus image using the
technique described above and to reject the mobile image if the
image focus score for the image falls below a predetermined
threshold without attempting to perform other image correction
techniques on the image. According to an embodiment, the image
correction module can send a message to the mobile device 340
indicating that the mobile image was too out of focus to be used
and requesting that the user retake the image.
[0116] The image correction module can be configured to first
identify the corners of a coupon or other document within a mobile
image (step 605). One technique that can be used to identify the
corners of the remittance coupon in a color image is illustrated in
FIG. 9 and is described in detail below. The corners of the
document can be defined by a set of points A, B, C, and D that
represent the corners of the document and define a quadrangle.
[0117] The image correction module can be configured to then build
a perspective transformation for the remittance coupon (step 610).
As can be seen in FIG. 1, the angle at which an image of a document
is taken can cause the rectangular shape of the remittance coupon
to appear distorted. FIG. 7 and its related description above
provide some examples of how a perspective transformation can be
constructed for a quadrangle defined by the corners A, B, C, and D
according to an embodiment. For example, the quadrangle identified
in step 605 can be mapped onto a same-sized rectangle in order to
build a perspective transformation that can be applied to the
document subimage, i.e. the portion of the mobile image that
corresponds to the remittance coupon, in order to correct
perspective distortion present in the image.
[0118] A geometrical transformation of the document subimage can be
performed using the perspective transformation built in step 610
(step 615). The geometrical transformation corrects the perspective
distortion present in the document subimage. An example of results
of geometrical transformation can be seen in FIG. 2 where a
document subimage of the remittance coupon pictured in FIG. 1 has
been geometrically corrected to remove perspective distortion.
[0119] A "dewarping" operation can also be performed on the
document subimage (step 620). An example of a warping of a document
in a mobile image is provided in FIG. 35. Warping can occur when a
document to be imaged is not perfectly flat or is placed on a
surface that is not perfectly flat, causing distortions in the
document subimage. A technique for identifying warping in a
document subimage is illustrated in FIG. 36.
[0120] According to an embodiment, the document subimage can also
binarized (step 625). A binarization operation can generate a
bi-tonal image with color depth of 1 bit per a pixel (1 bit/pixel).
Some automated processing systems, such as some Remote Deposit
systems require bi-tonal images as inputs. A technique for
generating a bi-tonal image is described below with respect to FIG.
10. FIG. 11 illustrates a binarized version of the geometrically
corrected mobile document image of the remittance coupon
illustrated in FIG. 2. As illustrated, in the bi-tonal image of
FIG. 11, the necessary information, such as payees, amounts,
account number, etc., has been preserved, while extra information
has been removed. For example, background patterns that might be
printed on the coupon are not present in the bi-tonal image of the
remittance coupon. Binarization of the subimage also can be used to
remove shadows and other defects caused by unequal brightness of
the subimage.
[0121] Once the image has been binarized, the code line of the
remittance coupon can be identified and read (step 630). As
described above, many remittance coupons include a code line that
comprises computer-readable text that can be used to encode
account-related information that can be used to reconcile a payment
received with the account for which the payment is being made. Code
line 205 of FIG. 2 illustrates an example of code line on a
remittance coupon.
[0122] Often, a standard optical character recognition font, the
OCR-A font, is used for printing the characters comprising the code
line. The OCR-A font is a fixed-width font where the characters are
typically spaced 0.10 inches apart. Because the OCR-A font is a
standardized fixed-width font, the image correction module can use
this information to determining a scaling factor for the image of
the remittance coupon. The scaling factor to be used can vary from
image to image, because the scaling is dependent upon the position
of the camera or other image capture device relative to the
document being imaged and can also be dependent upon optical
characteristics of the device used to capture the image of the
document. FIG. 20 illustrates a scaling method that can be used to
determine a scaling factor to be applied according to an
embodiment. The method illustrated in FIG. 20 is related to scaling
performed on a MICR-line of a check, but can be used to determined
a scaling factor for an image of a remittance coupon based on the
size of the text in the code line of the image of the remittance
coupon.
[0123] Once the scaling factor for the image has been determined, a
final geometrical transformation of the document image can be
performed using the scaling factor (step 635). This step is similar
to that in step 615, except the scaling factor is used to create a
geometrically altered subimage that represents the actual size of
the coupon at a given resolution. According to an embodiment, the
dimensions of the geometrically corrected image produced by set 635
are identical to the dimensions of an image produced by a flat bed
scanner at the same resolution.
[0124] During step 635, other geometrical corrections can also be
made, such as correcting orientation of the coupon subimage. The
orientation of the coupon subimage can be determined based on the
orientation of the text of the code line.
[0125] Once the final geometrical transformation has been applied,
a final adaptive binarization can be performed on the grayscale
image generated in step 635 (step 640). The bi-tonal image output
by the this step will have the correct dimensions for the
remittance coupon because the bi-tonal image is generated using the
geometrically corrected image generated in step 635.
[0126] According to an embodiment, the image correction module can
be configured to use several different binarization parameters to
generate two or more bi-tonal images of the remittance coupon. The
use of multiple images can improve data capture results. The use of
multiple bi-tonal images to improve data captures results is
described in greater detail below.
Detecting Document within Color Mobile Image
[0127] Referring now to FIG. 9, a flowchart is provided
illustrating an example method for automatic document detection
within a color image from a mobile device. According to an
embodiment, the method illustrated in FIG. 9 can be used to
implement step 605 of the method illustrated in FIG. 6. Typically,
the operations described within method of FIG. 9 are performed
within an automatic document detection module of the mobile
remittance server 310; however, embodiments exist where the
operations reside in multiple modules. In addition, generally the
automatic document detection module takes a variety of factors into
consideration when detecting the document in the mobile image. The
automatic document detection module can take into consideration
arbitrary location of the document within the mobile image, the 3-D
distortions within the mobile image, the unknown size of the
document, the unknown color of the document, the unknown color(s)
of the background, and various other characteristics of the mobile
engine, e.g. resolution, dimensions, etc.
[0128] The method of FIG. 9 begins at step 902 by receiving the
original color image from the mobile device. Upon receipt, this
original color image is converted into a smaller color image, also
referred to as a color "icon" image, at operation 904. This color
"icon" image preserves the color contrasts between the document and
the background, while suppressing contrasts inside the document. A
detailed description of an example conversion process is provided
with respect to FIG. 12.
[0129] A color reduction operation is then applied to the color
"icon" image at step 906. During the operation, the overall color
of the image can be reduced, while the contrast between the
document and its background can be preserved within the image.
Specifically, the color "icon" image of operation 904 can be
converted into a gray "icon" image (also known as a gray-scale
"icon" image) having the same size. An example, color depth
reduction process is described with further detail with respect to
FIG. 14.
[0130] The corners of the document are then identified within the
gray "icon" image (step 910). As previously noted above with
respect to FIG. 7, these corners A, B, C, and D make up the
quadrangle ABCD (e.g. quadrangle ABCD 2502). Quadrangle ABCD, in
turn, makes up the perimeter of the document. Upon detection of the
corners, the location of the corners is outputted (step 910).
Binarization
[0131] FIG. 10 illustrates a binarization method that can be used
to generate a bi-tonal image from a document image according to an
embodiment. The method illustrated in FIG. 10 can be used to
implement the binarization step 625 of the method illustrated in
FIG. 6. In an embodiment, the steps of the method illustrated in
FIG. 10 can be performed within module of the mobile remittance
server 310.
[0132] A binarization operation generates a bi-tonal image with
color depth of 1 bit per a pixel (1 bit/pixel). In the case of
documents, such as checks and deposit coupons, a bi-tonal image is
required for processing by automated systems, such as Remote
Deposit systems. In addition, many image processing engines require
such an image as input. The method of FIG. 10 illustrates
binarization of a gray-scale image of a document as produced by
geometrical operation 1004. This particular embodiment uses a novel
variation of well-known Niblack's method of binarization. As such,
there is an assumption that the gray-scale image received has a the
dimensions W pixel.times.H pixels and an intensity function I(x,y)
gives the intensity of a pixel at location (x,y) in terms one of
256 possible gray-shade values (8 bit/pixel). The binarization
operation will convert the 256 gray-shade value to a 2 shade value
(1 bit/pixel), using an intensity function B(x,y). In addition, to
apply the method, a sliding window with dimensions w pixels.times.h
pixels is defined and a threshold T for local (in-window) standard
deviation of gray image intensity I(x,y) is defined. The values of
w, h, and T are all experimentally determined.
[0133] A gray-scale image of the document is received at step 1602,
the method 1600 chooses a pixel p(x,y) within the image at step
1604. In FIG. 10, the average (mean) value ave and standard
deviation .sigma. of the chosen pixel's intensity I(x,y) within the
w.times.h current window location (neighborhood) of pixel p(x,y)
are computed (step 1606). If the standard deviation 6 is determined
to be too small at operation 1608 (i.e. .sigma.<T), pixel p(x,y)
is considered to low-contrast and, thus, part of the background.
Accordingly, at step 1610, low-contrast pixels are converted to
white, i.e. set B(x,y) set to 1, which is white; however, if the
deviation .sigma. is determined to be larger or equal to the
threshold T, i.e. .sigma..gtoreq.T, the pixel p(x,y) is considered
to be part of the foreground. In step 1612, if
I(p)<ave-k*.sigma., pixel p is considered to be a foreground
pixel and therefore B(x,y) is set to 0 (black). Otherwise, the
pixel is treated as background and therefore B(x,y) is set to 1. In
the formula above, k is an experimentally established
coefficient.
[0134] Subsequent to the conversion of the pixel at either step
1610 or operation 1612, the next pixel is chosen at step 1614, and
operation 1606 is repeated until all the gray-scale pixels (8
bit/pixel) are converted to a bi-tonal pixel (1 bit/pixel).
However, if no more pixels remain to be converted 1618, the
bi-tonal image of the document is then outputted at step 1620.
Conversion of Color Image to Icon Image
[0135] Referring now to FIG. 12, a flowchart is provided describing
an example method for conversion of a color image to a smaller
"icon" image according to an embodiment. This method can be used to
implement step 904 of the method illustrated FIG. 9. The smaller
"icon" image preserves the color contrasts between the document
depicted therein and its background, while suppressing contrasts
inside the document. Upon receipt of the original color image from
the mobile device (step 1201), over-sharpening is eliminated within
the image (step 1202). Accordingly, assuming the color input image
I has the dimensions of W.times.H pixels, operation 1202 averages
the intensity of image I and downscales image I to image I', such
that image I' has dimensions that are half that of image I (i.e.
W'=W/2 and H'=H/2). Under certain embodiments, the color
transformation formula can be described as the following:
C(p')=ave {C(q): q in S.times.S-window of p}, (eq. 5)
where [0136] C is any of red, green or blue components of color
intensity; [0137] p' is any arbitrary pixel on image I' with
coordinates (x',y'); [0138] p is a corresponding pixel on image
I:p=p(x,y), where x=2*x' and y=2*y'; [0139] q is any pixel included
into S.times.S-window centered in p; [0140] S is established
experimentally; and [0141] ave is averaging over all q in the
S.times.S-window.
[0142] Small "dark" objects within the image can then be eliminated
(step 1204). Examples of such small "dark" objects include, but are
not limited to, machine-printed characters and hand-printed
characters inside the document. Hence, assuming operation 1204
receives image I' from step 1202, step 1204 creates a new color
image I'' referred to as an "icon" with width W'' set to a fixed
small value and height H'' set to W''*(H/W), thereby preserving the
original aspect ratio of image I. In some embodiments, the
transformation formula can be described as the following:
C(p'')=max {C(q'): q' in S'.times.S'-window of p'}, (eq. 6)
where [0143] C is any of red, green or blue components of color
intensity; [0144] p'' is an arbitrary pixel on image I''; [0145] p'
is a pixel on image I' which corresponds to p'' under similarity
transformation, as previously defined; [0146] q' is any pixel on
image I' included into S'.times.S'-window centered in p'; [0147]
max is maximum over all q' in the S'.times.S'-window; [0148] W'' is
established experimentally; [0149] S' is established experimentally
for computing the intensity I''; and [0150] I''(p'') is the
intensity value defined by maximizing the intensity function I'
(p') within the window of corresponding pixel p' on image I',
separately for each color plane. The reason for using the "maximum"
rather than "average" is to make the "icon" whiter (white pixels
have a RGB-value of (255,255,255)).
[0151] In the next operation 1206, the high local contrast of
"small" objects, such as lines, text, and handwriting on a
document, is suppressed, while the other object edges within the
"icon" are preserved. Often, these other object edges are bold. In
various embodiments of the invention, multiple dilation and erosion
operations, also known as morphological image transformations, are
utilized in the suppression of the high local contrast of "small"
objects. Such morphological image transformations are commonly
known and used by those of ordinary skill in the art. The sequence
and amount of dilation and erosion operations used is determined
experimentally. Subsequent to the suppression operation 1206, a
color "icon" image is outputted at operation 1208. FIG. 13B depicts
an example of the mobile image of a check illustrated in FIG. 13A
after being converted into a color "icon" image according to an
embodiment.
Color Depth Reduction
[0152] Referring now to FIG. 14, a flowchart is provided
illustrating an example method that provides further details with
respect to the color depth reduction operation 906 as illustrated
in FIG. 9. At step 1301, a color "icon" image for color reduction
is received. The color "icon" image is divided into a grid (or
matrix) of fixed length and width with equal size grid elements at
operation 1302. In some embodiments, the preferred grid size is
such that there is a center grid element. For example, a grid size
of 3.times.3 may be employed. FIG. 15A depicts an example of the
color "icon" image of FIG. 13B after operation 1302 has divided it
into a 3.times.3 grid in accordance with one embodiment of the
invention.
[0153] Then, at step 1304, the "central part" of the icon, which is
usually the center most grid element, has its color averaged. Next,
the average color of the remaining parts of the icon is computed at
step 1306. More specifically, the grid elements "outside" the
"central part" of the "icon" have their colors averaged. Usually,
in instances where there is a central grid element, e.g. 3.times.3
grid, the "outside" of the "central part" comprises all the grid
elements other than the central grid element.
[0154] Subsequently, a linear transformation for the RGB-space is
determined at step 1308. The linear transformation is defined such
that it maps the average color of the "central part" computed
during operation 1304 to white, i.e. 255, while the average color
of the "outside" computed during operation 1306 maps to black, i.e.
0. All remaining colors are linearly mapped to a shade of gray.
This linear transformation, once determined, is used at operation
1310 to transform all RGB-values from the color "icon" to a
gray-scale "icon" image, which is then outputted at operation 1312.
Within particular embodiments, the resulting gray "icon" image,
also referred to as a gray-scale "icon" image, maximizes the
contrast between the document background, assuming that the
document is located close to the center of the image and the
background. FIG. 15B depicts an example of the color "icon" image
of FIG. 13B once it has been converted to a gray "icon" image in
accordance with one embodiment.
[0155] Referring now to FIG. 16, a flowchart is provided
illustrating an example method for finding document corners from a
gray "icon" image containing a document. The method illustrated in
FIG. 16 can be used to implement step 908 of the method illustrated
in FIG. 9. Upon receiving a gray "icon" image at operation 1401,
the "voting" points on the gray "icon" image are found in step 1402
for each side of the document depicted in the image. Consequently,
all positions on the gray "icon" image that could be approximated
with straight line segments to represent left, top, right, and
bottom sides of the document are found.
[0156] In accordance with one embodiment, this goal is achieved by
first looking for the "voting" points in the half of the "icon"
that corresponds with the current side of interest. For instance,
if the current side of interest is the document's top side, the
upper part of the "icon" (Y<H/2) is examined while the bottom
part of the "icon" (Y.gtoreq.H/2) is ignored.
[0157] Within the selected half of the "icon," the intensity
gradient (contrast) in the correct direction of each pixel is
computed. This is accomplished in some embodiments by considering a
small window centered in the pixel and, then, breaking the window
into an expected "background" half where the gray intensity is
smaller, i.e. where it is supposed to be darker, and into an
expected "doc" half where the gray intensity is higher, i.e. where
it is supposed to be whiter. There is a break line between the two
halves, either horizontal or vertical depending on side of the
document sought to be found. Next the average gray intensity in
each half-window is computed, resulting in an average image
intensity for the "background" and an average image intensity of
the "doc." The intensity gradient of the pixel is calculated by
subtracting the average image intensity for the "background" from
the average image intensity for the "doc."
[0158] Eventually, those pixels with sufficient gray intensity
gradient in the correct direction are marked as "voting" points for
the selected side. The sufficiency of the actual gray intensity
gradient threshold for determining is established
experimentally.
[0159] Continuing with method 1400, candidate sides, i.e. line
segments that potentially represent the sides of the document, i.e.
left, top, right, and bottom sides, are found. In order to do so,
some embodiments find all subsets within the "voting" points
determined in step 1402 that could be approximated by a straight
line segment (linear approximation). In many embodiments, the
threshold for linear approximation is established experimentally.
This subset of lines is defined as the side "candidates." As an
assurance that the set of side candidates is never empty, the gray
"icon" image's corresponding top, bottom, left, and right sides are
also added to the set.
[0160] Next, in step 1406 chooses the best candidate for each side
of the document from the set of candidates selected in operation
1404, thereby defining the position of the document within the gray
"icon" image. In accordance with some embodiments, the following
process is used in choosing the best candidate for each side of the
document:
[0161] The process starts with selecting a quadruple of line
segments {L, T, R, B}, where L is one of the candidates for the
left side of the document, T is one of the candidates for the top
side of the document, R is one of the candidates for the right side
of the document, and B is one of the candidates for the bottom side
of the document. The process then measures the following
characteristics for the quadruple currently selected.
[0162] The amount of "voting" points is approximated and measured
for all line segments for all four sides. This amount value is
based on the assumption that the document's sides are linear and
there is a significant color contrast along them. The larger values
of this characteristic increase the overall quadruple rank.
[0163] The sum of all intensity gradients over all voting points of
all line segments is measured. This sum value is also based on the
assumption that the document's sides are linear and there is a
significant color contrast along them. Again, the larger values of
this characteristic increase the overall quadruple rank.
[0164] The total length of the segments is measured. This length
value is based on the assumption that the document occupies a large
portion of the image. Again, the larger values of this
characteristic increase the overall quadruple rank.
[0165] The maximum of gaps in each corner is measured. For example,
the gap in the left/top corner is defined by the distance between
the uppermost point in the L-segment and the leftmost point in the
T-segment. This maximum value is based on how well the
side-candidates suit the assumption that the document's shape is
quadrangle. The smaller values of this characteristic increase the
overall quadruple rank.
[0166] The maximum of two angles between opposite segments, i.e.
between L and R, and between T and R, is measured. This maximum
value is based on how well the side-candidates suit the assumption
that the document's shape is close to parallelogram. The smaller
values of this characteristic increase the overall quadruple
rank.
[0167] The deviation of the quadruple's aspect ratio from the
"ideal" document aspect ratio is measured. This characteristic is
applicable to documents with a known aspect ratio, e.g. checks. If
the aspect ratio is unknown, this characteristic should be excluded
from computing the quadruple's rank. The quadruple's aspect ratio
is computed as follows: [0168] a) Find the quadrangle by
intersecting the quadruple's elements; [0169] b) Find middle-point
of each of the four quadrangle's sides; [0170] c) Compute distances
between middle-points of opposite sides, say D1 and D2; [0171] d)
Find the larger of the two ratios: R=max(D1/D2, D2/D1); [0172] e)
Assuming that the "ideal" document's aspect ratio is known and
Min/MaxAspectRatio represent minimum and maximum of the aspect
ratio respectively, define the deviation in question as: [0173] 0,
if MinAspectRatio<=R<=MaxAspectRatio [0174] MinAspectRatio-R,
if R<MinAspectRatio [0175] R-MaxAspectRatio, if
R>MaxAspectRatio. [0176] f) For checks, MinAspectRatio can be
set to 2.0 and MaxAspectRatio can be set to 3.0. This aspect ratio
value is based on the assumption that the document's shape is
somewhat preserved during the perspective transformation. The
smaller values of this characteristic increase the overall
quadruple rank.
[0177] Following the measurement of the characteristics of the
quadruple noted above, the quadruple characteristics are combined
into a single value, called the quadruple rank, using weighted
linear combination. Positive weights are assigned for the amount of
"voting" points, the sum all of intensity gradients, and the total
length of the segments. Negatives weights are assigned for maximum
gaps in each corner, maximum two angles between opposite segments,
and the deviation of the quadruple's aspect ratio. The exact values
of each of the weights are established experimentally.
[0178] The operations set forth above are repeated for all possible
combinations of side candidates, eventually leading to the "best"
quadruple, which is the quadruple with the highest rank. The
document's corners are defined as intersections of the "best"
quadruple's sides, i.e. the best side candidates.
[0179] In, step 1408 the corners of the document are defined using
the intersections of the best side candidates. A person of ordinary
skill in the art would appreciate that these corners can then be
located on the original mobile image by transforming the corner
locations found on the "icon" using the similarity transformation
previously mentioned. Method 1400 concludes at step 1410 where the
locations of the corners defined in step 1408 are output.
Geometric Correction
[0180] FIG. 17 provides a flowchart that illustrates an example
method for geometric correction in accordance with the invention
according to an embodiment. According to an embodiment, the method
illustrated in FIG. 17 can be used to implement steps 610, 615, and
635 of the method illustrated in FIG. 6. As previously mentioned,
geometric correction is needed to correct any possibly perspective
distortions that exist in the original mobile image. Additionally,
geometric correction can correct the orientation of the
documentation within the original mobile image, e.g. document is
orientated at 90, 180, or 270 degrees where the right-side-up
orientation is 0 degrees. It should be noted that in some
embodiments, the orientation of the document depends on the type of
document depicted in the mobile image, as well as the fields of
relevance on the document.
[0181] In instances where the document is in landscape orientation
(90 or 270 degrees), as illustrated by the check in FIG. 18A,
geometric correction is suitable for correcting the orientation of
the document. Where the document is at 180 degree orientation,
detection of the 180 degree orientation and its subsequent
correction are suitable when attempting to locate an object of
relevance on the document. A codeline for a remittance coupon can
be located in various locations on the remittance coupon, and might
not be located along the bottom of the coupon. The ability to
detect a codeline in an image of the remittance coupon changes
significantly after the document has been rotated 180-degrees. In
contrast, the MICR-line of check is generally known to be at a
specific location along the bottom of the document, and the
MICR-line can be used to determine the current orientation of the
check within the mobile image. In some embodiments, the object of
relevance on a document depends on the document's type. For
example, where the document is a contract, the object of relevance
may be a notary seal, signature, or watermark positioned at a known
position on the contract. Greater detail regarding correction of a
document (specifically, a check) having upside-down orientation
(180 degree orientation) is provided with respect to FIG. 19.
[0182] According to some embodiments, a mathematical model of
projective transformations is built and converts the distorted
image into a rectangle-shaped image of predefined size. According
to an embodiment, this step corresponds to step 610 of FIG. 6. In
an example, where the document depicted in mobile image is a check,
the predefined size is established as 1200.times.560 pixels, which
is roughly equivalent to the dimensions of a personal check scanned
at 200 DPI. In other embodiments, where the document depicted is a
remittance coupon, the size of the remittance coupons may not be
standardized. However, the size and spacing of the characters
comprising the code line can be used to determine a scaling factor
to be applied to the image to correct the size of the image of the
remittance coupon relative to a specific resolution.
[0183] Continuing with reference to the method of FIG. 17, there
are two separate paths of operations that are either performed
sequentially or concurrently, the outputs of which are eventually
utilized in the final output. One path of operations begins at step
1504 where the original mobile image in color is received. In step
1508, the color depth of the original mobile image is reduced from
a color image with 24 bit per a pixel (24 bit/pixel) to a
gray-scale image with 8 bit per a pixel (8 bit/pixel). This image
is subsequently outputted to step 1516 as a result of step
1512.
[0184] The other path of operations begins at step 1502, where the
positions of the document's corners within the gray "icon" image
are received. Based off the location of the corners, the
orientation of the document is determined and the orientation is
corrected (step 1506). In some embodiments, this operation uses the
corner locations to measure the aspect ratio of the document within
the original image. Subsequently, a middle-point between each set
of corners can be found, wherein each set of corners corresponds to
one of the four sides of the depicted document, resulting in the
left (L), top (T), right (R), and bottom (B) middle-points (step
1506). The distance between the L to R middle-points and the T to B
middle points are then compared to determine which of the two pairs
has the larger distance. This provides step 1506 with the
orientation of the document.
[0185] In some instances, the correct orientation of the document
depends on the type of document that is detected. For example, as
illustrated in FIG. 18A, where the document of interest is a check,
the document is determined to be in landscape orientation when the
distance between the top middle-point and bottom middle-point is
larger than the distance between the left middle-point and the
right middle-point. The opposite might be true for other types of
documents.
[0186] If it is determined in step 1506 that an orientation
correction is necessary, then the corners of the document are
shifted in a loop, clock-wise in some embodiments and
counter-clockwise in other embodiments.
[0187] At step 1510, the projective transformation is built to map
the image of the document to a predefined target image size of
width of W pixels and height of H pixels. In some embodiments, the
projective transformation maps the corners A, B, C, and D of the
document as follows: corner A to (0,0), corner B to (W,0), corner C
to (W,H), and corner D to (0,H). Algorithms for building projective
transformation are commonly known and used amongst those of
ordinary skill in the art.
[0188] At step 1516, the projective transformation created during
step 1514 is applied to the mobile image in gray-scale as outputted
as a result of step 1512. The projective transformation as applied
to the gray-scale image of step 1512 results in all the pixels
within the quadrangle ABCD depicted in the gray-scale image mapping
to a geometrically corrected, gray-scale image of the document
alone. FIG. 18B is an example gray-scale image of the document
depicted in FIG. 13A once a geometrical correction operation in
accordance with the invention is applied thereto. The process
concludes at operation 1518 where the gray-scale image of the
document is outputted to the next operation.
Correcting Landscape Orientation
[0189] FIG. 19 is a flow chart illustrating a method for correcting
landscape orientation of a document image according to an
embodiment. As previously noted, the geometric correction operation
as described in FIG. 17 is one method in accordance with the
invention for correcting a document having landscape orientation
within the mobile image. However, even after the landscape
orientation correction, the document still may remain in
upside-down orientation. In order to the correct upside-down
orientation for certain documents, some embodiments of the
invention require the image containing the document be binarized
beforehand. Hence, the orientation correction operation included in
step 635 usually follows the binarization operation of 625. While
the embodiment described herein uses the MICR-line of a check or
determine the orientation of an image, the code line of a
remittance coupon can be used to determine the orientation of a
remittance coupon using the technique described herein.
[0190] Upon receiving the bi-tonal image of the check at operation
1702, the MICR-line at the bottom of the bi-tonal check image is
read at operation 1704 and an MICR-confidence value is generated.
This MICR-confidence value (MC1) is compared to a threshold value T
at operation 1706 to determine whether the check is right-side-up.
If MC1>T at operation 1708, then the bi-tonal image of the check
is right side up and is outputted at operation 1710.
[0191] However, if MC1.ltoreq.T at operation 1708, then the image
is rotated 180 degrees at operation 1712, the MICR-line at the
bottom read again, and a new MICR-confidence value generated (MC2).
The rotation of the image by 180 degree is done by methods
commonly-known in the art. The MICR-confidence value after rotation
(MC2) is compared to the previous MICR-confidence value (MC1) plus
a Delta at operation 1714 to determine if the check is now
right-side-up. If MC2>MC2+Delta at operation 1716, the rotated
bi-tonal image has the check right-side-up and, thus, the rotated
image is outputted at operation 1718. Otherwise, if
MC2.ltoreq.MC2+Delta at operation 1716, the original bi-tonal image
of the check is right-side-up and outputted at operation 1710.
Delta is a positive value selected experimentally that reflects a
higher apriori probability of the document initially being
right-side-up than upside-down.
Size Correction
[0192] FIG. 20 provides a flowchart illustrating an example method
for size correction of an image according to an embodiment. The
method of FIG. 20 can be used to implement the size correction step
described in relation to step 630 of FIG. 6. Specifically, FIG. 20
illustrates an example method, in accordance with one embodiment,
for correcting the size of a check within a bi-tonal image, where
the check is oriented right-side-up. A person of ordinary skill in
the art would understand and appreciate that this method can
operate differently for other types of documents, e.g. deposit
coupons, remittance coupons.
[0193] Since many image processing engines are sensitive to image
size, it is crucial that the size of the document image be
corrected before it can be properly processed. For example, a form
identification engine may rely on the document size as an important
characteristic for identifying the type of document that is being
processed. Generally, for financial documents such as checks, the
image size should be equivalent to the image size produced by a
standard scanner running at 200 DPI.
[0194] In addition, where the document is a check, during the
geometric correction operation of some embodiments of the
invention, the geometrically corrected predefined image size is at
1200.times.560 pixels (See, for e.g., FIG. 15 description), which
is roughly equivalent to the size of a personal check scanned at
200 DPI; however, the size of business checks tend to vary
significantly, with most business checks having a width greater
than 1200 pixels when scanned at 200 DPI. Some business checks are
known to be as wide as 8.75'', which translates to be 1750 pixels
in width when scanned at 200 DPI. Hence, in order to restore the
size of business checks that have been geometrically corrected in
accordance with the invention at a predefined image size of
1200.times.560 pixels, the size correction operation is
performed.
[0195] Referring now to FIG. 20, after receiving a bi-tonal image
containing a check that is orientated right-side-up at operation
1802, the MICR-line at the bottom of the check is read at operation
1804. This allows the average width of the MICR-characters to be
computed at operation 1806. In doing so, the computer average width
gets compared to the average size of an MICR-character at 200 DPI
at operation 1808, and a scaling factor is computed accordingly. In
some embodiments of the invention, the scaling factor SF is
computer as follows:
SF=AW.sub.200/AW, (eq. 7)
where [0196] AW is the average width of the MICR-character found;
and [0197] A W.sub.200 is the corresponding "theoretical" value
based on the ANSI x9.37 standard (Specifications for Electronic
Exchange of Check and Image Data) at 200 DPI.
[0198] The scaling factor is used at operation 1810 to determine
whether the bi-tonal image of the check requires size correction.
If the scaling SF is determined to be less than or equal to
1.0+Delta, then the most recent versions of the check's bi-tonal
image and the check's the gray-scale image are output at operation
1812. Delta defines the system's tolerance to wrong image size.
[0199] If, however, the scaling factor SF is determined to be
higher than 1.0+Delta, then at operation 1814 the new dimensions of
the check are computed as follows:
AR=H.sub.S/W.sub.S (eq. 8)
W'=W*SF (eq. 9)
H'=AR*W', (eq. 10)
where [0200] H.sub.S and W.sub.S are the height and width of the
check snippet found on the original image; [0201] AR is the check
aspect ratio which we want to maintain while changing the size;
[0202] W is the width of geometrically corrected image before it's
size is adjusted; [0203] W' is the adjusted check's width in
pixels; and [0204] H' is the adjusted check's height in pixels.
Subsequent to re-computing the new dimensions, operation 1814
repeats geometrical correction and binarization using the newly
dimensioned check image. Following the repeated operations,
operation 1812 outputs the resulting bi-tonal image of the check
and gray-scale image of the check.
Image Quality Assurance
[0205] Once the mobile remittance server 310 has processed a mobile
image (see step 510 of the method illustrated in FIG. 5), the
mobile remittance server 310 can be configured to perform image
quality assurance processing on the mobile image to determine
whether the quality of the image is sufficient to submit to a
remittance processor 215.
[0206] FIG. 21 illustrates a mobile document image processing
engine (MDIPE) module 2100 for performing quality assurance testing
on mobile document images according to an embodiment. The MDIPE
module 2100 can receive a mobile document image captured by a
mobile device, or multiple mobile images for some tests; perform
preprocessing on the mobile document image; select tests to be
performed on the mobile document image; and execute the selected
tests to determine whether the quality of the image of a high
enough quality for a particular mobile application. The MDIPE
module 2100 includes a preprocessing module 2110 and test execution
module 2130. The preprocessing module 2110 can be configured to
receive a mobile image 2105 captured using a camera of a mobile
device as well as processing parameters 2107. According to an
embodiment, the mobile image 2105 and the processing parameters
2107 can be passed to MDIPE 2100 by a mobile application on the
mobile device where the mobile application provides the mobile
image 2105 to the MDIPE 2100 to have the quality of the mobile
image 2105 assessed.
[0207] The processing parameters 2107 can include various
information that the MDIPE 2100 can use to determine which tests to
run on the mobile image 2105. For example, the processing
parameters 2107 can identify the type of device used to capture the
mobile image 2105, the type of mobile application that will be used
to process the mobile image if the mobile image passes the IQA
testing, or both. The MDIPE 2100 can use this information to
determine which tests to select from test data store 2132 and which
test parameters to select from test parameter data store 2134. For
example, if a mobile image is being tested for a mobile deposit
application that expects an image of a check, a specific set of
tests related to assessing the image quality for a mobile image of
a check can be selected, such as an MICR-line test, or a test for
whether an image is blurry, etc. The MDIPE 2100 can also select
test parameters from test parameters data store 2134 that are
appropriate for the type of image to be processed, or for the type
of mobile device that was used to capture the image, or both. In an
embodiment, different parameters can be selected for different
mobile phones that are appropriate for the type of phone used to
capture the mobile image. For example, some mobile phones might not
include an autofocus feature.
[0208] The preprocessing module 2110 can process the mobile
document image to extract a document snippet that includes the
portion of the mobile document that actually contains the document
to be processed. This portion of the mobile document image is also
referred to herein as the document subimage. The preprocessing
module 2110 can also perform other processing on the document
snippet, such as converting the image to a grayscale or bi-tonal
document snippet, geometric correction of the document subimage to
remove view distortion, etc. Different tests can require different
types of preprocessing to be performed, and the preprocessing
module 2110 can produce mobile document snippets from a mobile
document image depending on the types of mobile IQA tests to be
executed on the mobile document image.
[0209] The test execution module 2130 receives the selected tests
and test parameters 2112 and the preprocessed document snippet (or
snippets) 120 from the preprocessing mobile 110. The test execution
module 2130 executes the selected tests on the document snippet
generated by the preprocessing module 2110. The test execution
module 2130 also uses the test parameters provided by the
preprocessing module 2110 when executing the test on the document
snippet. The selected tests can be a series of one or more tests to
be executed on the document snippets to determine whether the
mobile document image exhibits geometrical or other defects.
[0210] The test execution module 2130 executes each selected test
to obtain a test result value for that test. The test execution
module 2130 then compares that test result value to a threshold
value associated with the test. If the test result value is equal
to or exceeds the threshold, then the mobile image has passed the
test. Otherwise, if the test result value is less than the
threshold, the mobile document image has failed the test. According
to some embodiments, the test execution module 2130 can store the
test result values for the tests performed in test results data
store 2138.
[0211] According an embodiment, the test threshold for a test can
be stored in the test parameters data store 2134 and can be fetched
by the preprocessing module 2110 and included with the test
parameters 2112 provided to the test execution module 2130.
According to an embodiment, different thresholds can be associated
with a test based on the processing parameters 2107 received by the
preprocessing module 2110. For example, a lower threshold might be
used for an image focus IQA test for image capture by camera phones
that do not include an autofocus feature, while a higher threshold
might be used for the image focus IQA test for image capture by
camera phones that do include an autofocus feature.
[0212] According to an embodiment, a test can be flagged as
"affects overall status." These tests are also referred to here as
"critical" tests. If a mobile image fails a critical test, the
MDIPE 2100 rejects the image and can provide detailed information
to the mobile device user explaining why the image was not of a
high enough quality for the mobile application and that provides
guidance for retaking the image to correct the defects that caused
the mobile document image to fail the test, in the event that the
defect can be corrected by retaking the image.
[0213] According to an embodiment, the test result messages
provided by the MDIPE 2100 can be provided to the mobile
application that requested the MDIPE 2100 perform the quality
assurance testing on the mobile document image, and the mobile
application can display the test results to the user of the mobile
device. In certain embodiments, the mobile application can display
this information on the mobile device shortly after the user takes
the mobile document image to allow the user to retake the image if
the image is found to have defects that affect the overall status
of the image. In some embodiments, where the MDIPE 2100 is
implemented at least in part on the mobile device, the MDIPE 2100
can include a user interface module that is configured to display
the test results message on a screen of the mobile device.
[0214] FIG. 21 merely provides a description of the logical
components of the MDIPE 2100. In some embodiments, the MDIPE 2100
can be implemented on the mobile device 340, in software, hardware,
or a combination thereof. In other embodiments, the MDIPE 2100 can
be implemented on the mobile remittance server 310, and the mobile
device can send the mobile image 2105 and the processing parameters
2107, e.g., via a wireless interface, to the mobile remittance
server 310 for processing, and the mobile remittance server 310 can
send the test results and test messages 2140 to the mobile device
to indicate whether the mobile image passed testing. In some
embodiments, part of the functionality of the MDIPE 2100 can be
implemented on the mobile device while other parts of the MDIPE
2100 are implemented on the remote server. The MDIPE 2100 can be
implemented in software, hardware, or a combination thereof. In
still other embodiments, the MDIPE 2100 can be implemented entirely
on the remote server, and can be implemented using appropriate
software, hardware, or a combination there.
[0215] FIG. 22 is a flow diagram of a process for performing mobile
image quality assurance on an image captured by a mobile device
according to an embodiment. The process illustrated in FIG. 22 can
be performed using the MDIPE 2100 illustrated in FIG. 21.
[0216] The mobile image 2105 captured by a mobile device is
received (step 2205). The mobile image 2105 can also be accompanied
by one or more processing parameters 2107.
[0217] As described above, the MDIPE 2100 can be implemented on the
mobile device, and the mobile image can be provided by a camera
that is part of or coupled to the mobile device. In some
embodiments, the MDIPE 2100 can also be implemented at least in
part on a remote server, and the mobile image 2105 and the
processing parameters 2107 can be transmitted to the remove server,
e.g., via a wireless interface included in the mobile device.
[0218] Once the mobile image 2105 and the processing parameters
2107 have been received, the mobile image is processed to generate
a document snippet or snippets (step 2210). For example,
preprocessing module 2110 of MDIPE 2100 can be used to perform
various preprocessing on the mobile image. One part of this
preprocessing includes identifying a document subimage in the
mobile image. The subimage is the portion of the mobile document
image that includes the document. The preprocessing module 2110 can
also perform various preprocessing on the document subimage to
produce what is referred to herein as a "snippet." For example,
some tests can require that a grayscale image of the subimage be
created. The preprocessing module 2110 can create a grayscale
snippet that represents a grayscale version of the document
subimage. In another example, some tests can require that a bitonal
image of the subimage be created. The preprocessing module 2110 can
create a bitonal snippet that represents a bitonal version of the
document subimage. In some embodiments, the MDIPE 2100 can generate
multiple different snippets based on the types of tests to be
performed on the mobile document image.
[0219] After processing the mobile document image to generate a
snippet, the MDIPE 2100 then selects one or more tests to be
performed on the snippet or snippets (step 2215). In an embodiment,
the tests to be performed can be selected from test data store
2132. In an embodiment, the MDIPE 2100 selects the one or more
tests based on the processing parameters 2107 that were received
with the mobile image 2105.
[0220] After selecting the tests from the test data store 2132,
test parameters for each of the tests can be selected from the test
parameters data store 2134 (step 2220). According to an embodiment,
the test parameters can be used to configure or customize the tests
to be performed. For example, different test parameters can be used
to configure the tests to be more or less sensitive to certain
attributes of the mobile image. In an embodiment, the test
parameters can be selected based on the processing parameters 2107
received with the mobile image 2105. As described above, these
processing parameters can include information, such as the type of
mobile device used to capture the mobile image as well as the type
of mobile application that is going to be used to process the
mobile image if the mobile image passes scrutiny of the mobile
image IQA system.
[0221] Once the tests and the test parameters have been retrieved
and provided to the test execution module 2130, a test is selected
from tests to be executed, and the test is executed on the document
snippet to produce a test result value (step 2225). In some
embodiments, more than one document snippet may be used by a test.
For example, a test can be performed that tests whether images of a
front and back of a check are actually images of the same document
can be performed. The test engine can receive both an image of the
front of the check and an image of the back of the check from the
preprocessing module 2110 and use both of these images when
executing the test.
[0222] The test result value obtained by executing the test on the
snippet or snippets of the mobile document is then compared to test
threshold to determine whether the mobile image passes or fails the
test (step 2230) and a determination is made whether the test
results exceed the threshold (step 2235). According to an
embodiment, the test threshold can be configured or customized
based on the processing parameters 2107 received with the mobile
image. For example, the test for image blurriness can be configured
to use a higher threshold for passing if the image is to be used to
for a mobile deposit application where the MICR-line information
needs to be recognized and read from the document image. In
contrast, the test for blurriness can be configured use a lower
threshold for passing the mobile image for some mobile
applications. For example, the threshold for image quality may be
lowered for if a business card is being imaged rather than a check.
The test parameters can be adjusted to minimize the number of false
rejects and false accept rate, the number of images marked for
reviewing, or both.
[0223] The "affects overall status" flag of a test can also be
configured based on the processing parameters 2107. For example, a
test can be marked as not affecting the overall status for some
types of mobile applications or for documents being processed, or
both. Alternatively, a test can also be marked as affecting overall
status for other types of mobile applications or documents being
processed, or both. For example, a test that identifies the
MICR-line of a check can be marked as "affecting overall status" so
that if the MICR-line on the check cannot be identified in the
image, the image will fail the test and the image will be rejected.
In another example, if the mobile application is merely configured
to receive different types of mobile document image, the mobile
application can perform a MICR-line test on the mobile document
image in an attempt to determine whether the document that was
imaged was a check. In this example, the MICR-line may not be
present, because a document other than a check may have been
imaged. Therefore, the MICR-line test may be marked as not
"affecting overall status," and if a document fails the test, the
transaction might be flagged for review but not marked as
failed.
[0224] Since different camera phones can have cameras with very
different optical characteristics, image quality may vary
significantly between them. As a result, some image quality defects
may be avoidable on some camera phones and unavoidable on the
others and therefore require different configurations. To mitigate
the configuration problem, Mobile IQA test can be automatically
configured for different camera phones to use different tests, or
different thresholds for the tests, or both. For example, as
described above, a lower threshold can be used for an image focus
IQA test on mobile document images that are captured using a camera
phone that does not include an autofocus feature than would be used
for camera phones that do include an autofocus feature, because it
can be more difficult for a user to obtain as clear an image on
using a device that doesn't an autofocus feature.
[0225] In certain embodiments, if the test result exceeded or
equaled the threshold, the image passed the test and a
determination is made whether there are more tests to be executed
(step 2240). If there are more tests to be executed, the next test
can be selected and executed on the document snippet (step 2225).
Otherwise, if there were not more tests to be executed, the test
results, or test messages, or both are output by MDIPE 2100 (step
2270). There can be one or more test messages included with the
results if the mobile image failed one more of the tests that were
executed on the image.
[0226] In such embodiments, if the test result was less than the
threshold, then the mobile image has failed the test. A
determination is made whether the test affects the overall status
(step 250). If the test affects the overall status of the image,
detailed test result messages that explain why the image failed the
test can be loaded from the test message data store 134 (step 2255)
and the test result messages can be added to the test results (step
2260). The test results and test messages can then be output by the
MDIPE 2100 (step 2270).
[0227] Alternatively, if the test did not affect the overall
status, the test results can be loaded noted and the transaction
can be flagged for review (step 2265). By flagging the transaction
for review, a user of a mobile device can be presented with
information indicating that a mobile image has failed at least some
of the test that were performed on the image, but the image still
may be of sufficient quality for use with the mobile application.
The user can then be presented with the option to retake the image
or to send the mobile image to the mobile application for
processing. According to some embodiments, detailed test messages
can be loaded from the test message data store 134 for all tests
that fail and can be included with the test results, even if the
test is not one that affects the overall status of the mobile
image.
[0228] According to some embodiments, the mobile IQA test can also
be configured to eliminate repeated rejections of a mobile
document. For example, if an image of a check is rejected as have
too low a contrast by a contrast test, the image is rejected, and
the user can retake and resubmit the image via the mobile
application, the processing parameters 2107 received with the
mobile image can include a flag indicating that the image is being
resubmitted. In some embodiments, the thresholds associated with
the tests that the image failed can be lowered to see if the image
can pass the test with a lower threshold. In some embodiments, the
thresholds are only lowered for non-critical tests. According to an
embodiment, the processing parameters 2107 can also include a count
of the number of times that an image has been resubmitted and the
thresholds for a test are only lowered after a predetermined number
of times that the image is resubmitted.
[0229] FIG. 23 is a flow diagram of a process for performing mobile
image quality assurance on an image of a check captured by a mobile
device according to an embodiment. Like the process illustrated in
FIG. 22, the process illustrated in FIG. 23 can be performed using
the MDIPE 2100 illustrated in FIG. 21. The method illustrated in
FIG. 23 can be used where an image of a check is captured in
conjunction with a remittance payment. The method illustrated in
FIG. 23 can be used to assess the quality of the image of the
check.
[0230] The method illustrated in FIG. 23 illustrates how the mobile
IQA and MDIPE 2100 can be used with the electronic check processing
provided under the Check Clearing for the 21st Century Act. The
Check Clearing for the 21st Century Act (also referred to as the
"Check 21 Act") is a United States federal law (Pub.L. 108-100)
that was enacted on Oct. 28, 2003. The law allows the recipient of
a paper check to create a digital version of the original check
called a "substitute check," which can be processed, eliminating
the need to process the original physical document. The substitute
check includes an image of the front and back sides of the original
physical document. The mobile IQA tests can be used check the
quality of the images captured by a mobile device. The snippets
generated by the MDIPE 2100 can then be further tested by one or
more Check 21 mobile IQA tests that perform image quality assurance
on the snippets to determine whether the images meet the
requirements of the Check 21 Act as well.
[0231] The mobile image 2105 captured by a mobile device is
received (step 2305). In an embodiment, image of the front and back
sides of the check can be provided. The mobile image 2105 can also
be accompanied by one or more processing parameters 2107. Check
data can also be optionally received (step 2307). The check data
can be optionally provided by the user at the time that the check
is captured. This check data can include various information from
the check, such as the check amount, check number, routing
information from the face of the check, or other information, or a
combination thereof. In some embodiments, a mobile deposition
application requests this information from a user of the mobile
device, allows the user to capture an image of a check or to select
an image of a check that has already been captured, or both, and
the mobile deposit information provides the check image, the check
data, and other processing parameters to the MDIPE 2100.
[0232] Once the mobile image 2105, the processing parameters 2107,
and the check data have been received, the mobile image is
processed to generate a document snippet or snippets (step 2310).
As described above, the preprocessing can produce one or more
document snippets that include the portion of the mobile image in
which the document was located. The document snippets can also have
additional processing performed on them, such as conversion to a
bitonal image or to grayscale, depending on the types of testing to
be performed.
[0233] After processing the mobile document image to generate a
snippet, the MDIPE 2100 then selects one or more tests to be
performed on the snippet or snippets (step 2315). In an embodiment,
the tests to be performed can be selected from test data store
2132. In an embodiment, the MDIPE 2100 selects the one or more
tests based on the processing parameters 2107 that were received
with the mobile image 2105.
[0234] After selecting the tests from the test data store 2132,
test parameters for each of the tests can be selected from the test
parameters data store 2134 (step 2320). As described above, the
test parameters can be used to configure or customize the tests to
be performed.
[0235] Once the tests and the test parameters have been retrieved
and provided to the test execution module 2130, a test is selected
from tests to be executed, and the test is executed on the document
snippet to produce a test result value (step 2325). In some
embodiments, more than one document snippet can be used by a test.
For example, a test can be performed that tests whether images of a
front and back of a check are actually images of the same document
can be performed. The test engine can receive both an image of the
front of the check and an image of the back of the check from the
preprocessing module 2110 and use both of these images when
executing the test. Step 2325 can be repeated until each of the
tests to be executed is performed.
[0236] The test result values obtained by executing each test on
the snippet or snippets of the mobile document are then compared to
test threshold with that test to determine whether the mobile image
passes or fails the test (step 2330) and a determination can be
made whether the mobile image of the check passed the test
indicating that image quality of mobile image is acceptable (step
2335). If the mobile document image of the check passed, the MDIPE
2100 passes then executes one or more Check 21 tests on the
snippets (step 2340).
[0237] The test result values obtained by executing the Check 21
test or tests on the snippet or snippets of the mobile document are
then compared to test threshold with that test to determine whether
the mobile image passes or fails the test (step 2345) and a
determination can be made whether the mobile image of the check
passed the test indicating that image quality of mobile image is
acceptable under the requirements imposed by the Check 21 Act (step
2350). Step 345 can be repeated until each of the Check 21 tests is
performed. If the mobile document image of the check passed, the
MDIPE 2100 passes the snippet or snippets to the mobile application
for further processing (step 2370).
[0238] If the mobile document image of the check failed one or more
mobile IQA or Check 21 tests, detailed test result messages that
explain why the image failed the test can be loaded from the test
message data store 134 (step 2355) and the test result messages can
be added to the test results (step 2360). The test results and test
messages are then output to the mobile application where they can
be displayed to the user (step 2365). The user can use this
information to retake the image of the check in an attempt to
remedy some or all of the factors that caused the image of the
check to be rejected.
Mobile IQA Tests
[0239] FIGS. 24A-41 illustrate various sample mobile document
images and various testing methods that can be performed when
assessing the image quality of a mobile document image. As
described above, the preprocessing module 2110 can be configured to
extract the document subimage, also referred to herein as the
subimage, from the mobile document image. The subimage generally
will be non-rectangular because of perspective distortion; however,
the shape of the subimage can generally be assumed to be
quadrangular, unless the subimage is warped. Therefore, the
document can be identified by its four corners.
[0240] In some embodiments, a mobile IQA test generates a score for
the subimage on a scale that ranges from 0-1000, where "0"
indicates a subimage having very poor quality while a score of
"1000" indicates that the image is perfect according to the test
criteria.
[0241] Some tests use a geometrically corrected snippet of the
subimage to correct view distortion. The preprocessing module 2110
can generate the geometrically corrected snippet. FIG. 24A
illustrates a mobile image where the document captured in the
mobile document image exhibits view distortion. FIG. 24B
illustrates an example of a grayscale geometrically corrected
subimage generated from the distorted image in FIG. 24A.
Image Focus IQA Test
[0242] According to some embodiments, an Image Focus IQA Test can
be executed on a mobile image to determine whether the image is too
blurry to be used by a mobile application. Blurry images are often
unusable, and this test can help to identify such out-of-focus
images and reject them. The user can be provided detailed
information to assist the user in taking a better quality image of
the document. For example, the blurriness may have been the result
of motion blur caused by the user moving the camera while taking
the image. The test result messages can suggest that the user hold
the camera steadier when retaking the image.
[0243] Mobile devices can include cameras that have significantly
different optical characteristics. For example, a mobile device
that includes a camera that has an auto-focus feature can generally
produce much sharper images than a camera that does not include
such a feature. Therefore, the average image focus score for
different cameras can vary widely. As a result, the test threshold
can be set differently for different types of mobile devices. As
described above, the processing parameters 2107 received by MDIPE
2100 can include information that identifies the type of mobile
device and/or the camera characteristics of the camera used with
the device in order to determine what the threshold should be set
to for the Image Focus IQA Test.
[0244] An in-focus mobile document image, such as that illustrated
in FIG. 25A will receive a score of 1000, while an out of focus
document, such as that illustrated in FIG. 25B will receive a much
lower score, such as in the 50-100 range. Most of the time, images
are not completely out of focus. Therefore, a score of 0 is
uncommon.
[0245] According to an embodiment, the focus of the image can be
tested using various techniques, and the results can then be
normalized to the 0-1000 scale used by the MDIPE 2100.
[0246] In an embodiment, the Image Focus Score can be computed
using the following technique: The focus measure is a ratio of
maximum video gradient between adjacent pixels, measured over the
entire image and normalized with respect to image's gray level
dynamic range and "pixel pitch." According to an embodiment, the
image focus score can be calculated using the following equation
described in "The Financial Services Technology Consortium," Image
Defect Metrics, IMAGE QUALITY & USABILITY ASSURANCE: Phase 1
Project, Draft Version 1.0.4. May 2, 2005, which is hereby
incorporated by reference:
Image Focus Score=(Maximum Video Gradient)/[(Gray Level Dynamic
Range)*(Pixel Pitch)]
where Video Gradient=ABS [(Gray level for pixel "i")-(Gray level
for pixel "i+1")]
Gray Level Dynamic Range=[(Average of the "N" Lightest
Pixels)-(Average of the "N" Darkest Pixels)]
Pixel Pitch=[1/Image Resolution (in dpi)]
[0247] The variable N is equal to the number of pixels used to
determine the average darkest and lightest pixel gray levels in the
image. According to one embodiment, the value of N is set to 64.
Therefore, the 64 lightest pixels in the image are averaged
together and the 64 darkest pixels in the image are averaged
together, to compute the "Gray Level Dynamic" range value. The
resulting image focus score value is the multiplied by 10 in order
to bring the value into the 0-1000 range used for the test results
in the mobile IQA system.
[0248] The Image Focus Score determined using these techniques can
be compared to an image focus threshold to determine whether the
image is sufficiently in focus. As described above, the threshold
used for each test may be determined at least in part by the
processing parameters 2107 provided to MDIPE 2100. The Image Focus
score can be normalized to the 0-1000 range used by the mobile IQA
tests and compared to a threshold value associated with the test.
If the Image Focus Score meets or exceeds this threshold, then the
mobile document image is sufficiently focused for use with the
mobile application.
Shadow Test
[0249] According to some embodiments, a Shadow Test can be executed
on a mobile image to determine whether a portion of the image is
covered by a shadow. A shadow can render parts of a mobile image
unreadable. This test helps to identify whether a shadow coverage a
least a portion of a subimage in a mobile document image, and to
reject images if the shadow has too much of an effect on the image
quality, so that the user can attempt to take a better quality
image of the document where the shadow is not present.
[0250] According to an embodiment, the presence of a shadow is
measured by examining boundaries in the mobile image that intersect
two or more sides of the document subimage. FIG. 26 illustrates an
example of a shadowed document. The document subimage has been
extracted from the mobile document image and converted to a
grayscale snippet in this example. The shadow boundary clearly
intersects the top and the bottom of the check pictured in the
snippet.
[0251] The presence of shadows can be measured using the area and
contrast. If a shadow covers the entire image, the result is merely
an image that is darker overall. Such shadows generally do not
worsen image quality significantly. Furthermore, shadows having a
very small surface area also do not generally worsen image quality
very much.
[0252] According to an embodiment, the Image Shadowed Score can be
calculated using the following formula to determine the score for a
grayscale snippet:
Image Shadowed score=1000 if no shadows were found, otherwise
Image Shadowed score=1000-min (Score(S[i])), where Score(S[i]) is
computed for every shadow S[i] detected on the grayscale
snippet
[0253] In an embodiment, the Score for each shadow can be computed
using the following formula:
Given shadow S[i] in the grayscale image, the score can be
calculated Score(S[i]) as Score(S[i])=2000*min(A[i]/A,
1-A[i]/A)*(Contrast/256), where A[i] is the area covered by shadow
S[i] (in pixels), A is the entire grayscale snippet area (in
pixels), and Contrast is the difference of brightness inside and
outside of the shadow (the maximum value is 256).
[0254] Due to the normalization factor 2000, Score(S[i]) fits into
0-1000 range. It tends to assume larger values for shadows that
occupy about 1/2 of the snippet area and have high contrast.
Score(S[i]) is typically within 100-200 range. In an embodiment,
the Image Shadowed score calculated by this test falls within a
range of 0-1000 as do the test results from other tests. According
to an embodiment, a typical mobile document image with few shadows
will have a test result value in a range form 800-900. If no
shadows are on are found the document subimage, then the score will
equal 1000. The Image Shadowed score can then be compared to a
threshold associated with the test to determine whether the image
is of sufficiently high quality for use with the mobile application
requesting the assessment of the quality of the mobile document
image.
Contrast Test
[0255] According to some embodiments, a Contrast Test can be
executed on a mobile image to determine whether the contrast of the
image is sufficient for processing. One cause of poor contrast is
images taken with insufficient light. A resulting grayscale snippet
generated from the mobile document image can have low contrast, and
if the grayscale snippet is converted to a binary image, the
binarization module can erroneously white-out part of the
foreground, such as the MICR-line of a check, the code line of a
remittance coupon, an amount, or black-out part of the background.
The Contrast Test measures the contrast and rejects poor quality
images, and instructs the user to retake the picture under brighter
light to improve the contrast of the resulting snippets.
[0256] FIG. 28 illustrates a method for executing a Contrast IQA
Test according to an embodiment. The Contrast IQA Test illustrated
in FIG. 28 is performed on a grayscale snippet generated from a
mobile document image. The MDIPE 2100 receives the mobile image
(step 2805) and generates a grayscale snippet that comprises a
grayscale version of the document subimage (step 2810). FIG. 27 is
an example of a grayscale snippet generated from a mobile document
image of a check. As can be seen from FIG. 27, the contrast of the
image is very low.
[0257] A histogram of the grayscale values in the grayscale snippet
can then be built (step 2815). In an embodiment, the x-axis of the
histogram is divided into bins that each represents a "color" value
for the pixel in the grayscale image and the y-axis of the
histogram represents the frequency of that color value in the
grayscale image. According to an embodiment, the grayscale image
has pixel in a range from 0-255, and the histogram is built by
iterating through each value in this range and counting the number
of pixels in the grayscale image having this value. For example,
frequency of the "200" bin would include pixels having a gray value
of 200.
[0258] A median black value can then be determined for the
grayscale snippet (step 2820) and a median white value is also
determined for the grayscale snippet (step 2825). The median black
and white values can be determined using the histogram that was
built from the grayscale snippet. According to an embodiment, the
median black value can be determined by iterating through each bin,
starting with the "0" bin that represents pure black and moving
progressively toward the "250" bin which represents pure white.
Once a bin is found that includes at least 20% of the pixels
included in the image, the median black value is set to be the
color value associated with that bin. According to an embodiment,
the median white value can be determined by iterating through each
bin, starting with the "255" bin which represents pure white and
moving progressively toward the "0" bin which represents pure
black. Once a bin is found that includes at least 20% of the pixels
included in the image, the median white value is set to be the
color value associated with that bin.
[0259] Once the median black and white values have been determined,
the difference between the median black and white values can then
be calculated (step 2830). The difference can then be normalized to
fall within the 0-1000 test range used in the mobile IQA tests
executed by the MDIPE 2100 (step 2835). The test result value can
then be returned (step 2840). As described above, the test result
value is provided to the test execution module 2130 where the test
result value can be compared to a threshold value associated with
the test. See for example, FIG. 22, step 2230, described above. If
the mobile image fails the Contrast IQA Test, the MDIPE 2100 can
reject the image, and load detailed test messages from the test
message data store 134 that include detailed instructions that how
the user might retake the image.
Planar Skew Test
[0260] According to some embodiments, a Planar Skew Test can be
executed on a mobile image to determine whether the document
subimage is skewed within the mobile image. See FIG. 29A for an
example of a mobile document image that includes a remittance
coupon or check that exhibits significant planar skew. Planar skew
does not result in distortion of the document subimage; however, in
an embodiment, the subimage detection module included in the
preprocessing module assumes that the document subimage is nearly
horizontal in the mobile document image. If the skew becomes too
extreme, for example approaching 45 degrees from horizontal,
cropping errors could occur when the document subimage is extracted
from the mobile document image.
[0261] According to an embodiment, document skew can be measured by
first identifying the corners of the document subimage using one of
the techniques described above. The corners of the documents
subimage can be identified by the preprocessing module 130 when
performing projective transformations on the subimage, such as that
described above with respect to FIGS. 24A and 24B. Various
techniques for detecting the skew of the subimage can be used. For
example, techniques for detecting skew disclosed in the related
'071 and '091 Applications, can be used to detect the skew of the
subimage. The results from the skew test can then be to fall within
the 0-1000 test range used in the mobile IQA tests executed by the
MDIPE 2100. The higher the skew of the document subimage, the lower
the normalized test value. If the normalized test value falls below
the threshold value associated with the test, the mobile document
image can be rejected and the user can be provided detailed
information from the test result messages data store 136 for how to
retake the image and reduce the skew.
View Skew Test
[0262] "View skew" denotes a deviation from direction perpendicular
to the document in mobile document image. Unlike planar skew, the
view skew can result in the document subimage having perspective
distortion. FIG. 29B illustrates an example of a document subimage
that exhibits view skew. View skew can cause problems in processing
the subimage if the view skew becomes too great, because view skew
changes the width-to-height ratio of the subimage. This can present
a problem, since the true dimensions of the document pictured in
the subimage are often unknown. For example, remittance coupons and
business checks can be various sizes and can have different
width-to-height ratios. View skew can result in content recognition
errors, such as errors in recognition of the MICR-line data on a
check or CAR/LAR recognition (which stands for Courtesy Amount
Recognition and Legal Amount Recognition) or errors in recognition
of the code line of a remittance coupon. By measuring the view
skew, the view skew test can be used to reject images that have too
much view skew, which can help reduce false rejects and false
accepts rates by addressing an issue that can be easily corrected
by a user retaking the mobile document image.
[0263] FIG. 30 is a flow chart illustrating a method for testing
for view skew according to an embodiment. The MDIPE 2100 receives
the mobile image (step 3005) and identifies the corners of the
document within the subimage (step 3010). A skew test score can
then be determined for the document subimage (step 3015) and skew
test score can then be returned (3040). As described above, the
test result value can then be provided to the test execution module
2130 where the test result value can be compared to a threshold
value associated with the test.
[0264] According to an embodiment, the view skew of a mobile
document can be determined using the following formula:
View Skew score=1000-F(A,B,C,D),
where
F(A,B,C,D)=500*max(abs(|AB|-|CD|)/(|DA|+|BC|),
abs(|BC|-|DA|)/(|AB|+|CD|)), [0265] where |PQ| denotes the distance
from point P to point Q, and the corners of the subimage are
denoted as follows: A represents the top-left corner, B represents
the top-right corner of the subimage, C represents the bottom-right
corner of the subimage, and D represents the bottom-left corner of
the subimage.
[0266] One can see that View Skew score can be configured to fit
into [0, 1000] range used in the other mobile IQA tests described
herein. In this example, the View Skew score is equal to 1000 when
|AB|=|CD| and |BC|=|DA|, which is the case when there is no
perspective distortion in the mobile document image and
camera-to-document direction was exactly perpendicular. The View
Skew score can then be compared to a threshold value associated
with the test to determine whether the image quality is
sufficiently high for use with the mobile application.
Cut Corner Test
[0267] Depending upon how carefully the user framed a document when
capturing a mobile image, it is possible that one or more corners
of the document can be cut off in the mobile document image. As a
result, important information can be lost from the document. For
example, if the lower left-hand corner of a check is cut off in the
mobile image, a portion of the MICR-line of a check or the code
line of a remittance coupon might be cut off, resulting in
incomplete data recognition. FIG. 31 illustrates an example of a
mobile document image that features a receipt where one of the
corners has been cut off.
[0268] FIG. 32 illustrates a Cut-Off Corner Test that can be used
with embodiments of the MDIPE 2100 for testing whether corners of a
document in a document subimage have been cut off when the document
was imaged. The mobile image including height and width parameters
are received (step 3205). In an embodiment, the height and width of
the mobile image can be determined by the preprocessing module
2110. The corners of the document subimage are then identified in
the mobile document image (step 3210). Various techniques can be
used to identify the corners of the image, including the various
techniques described above. In an embodiment, the preprocessing
module 2110 identifies the corners of the document subimage. As
illustrated in FIG. 11, one or more of the corners of a document
can be cut off. However, the preprocessing module 2110 can be
configured to determine what the location of the corner should have
been had the document not been cut off using the edges of the
document in the subimage. FIG. 31 illustrates how the preprocessing
module 2110 has estimated the location of the missing corner of the
document by extending lines from the sides of the document out to
the point where the lines intersect. The preprocessing module 2110
can then provide the corners information for the document to the
test execution module 2130 to execute the Cut-Off Corner IQA Test.
In an embodiment, test variables and the test results values to be
returned by the test are set to default values: the test value V to
be returned from the test is set to a default value of 1000,
indicating that all of the corners of the document are within the
mobile document image, and a maximum cut off variable (MaxCutOff)
is set to zero indicating that no corner was cut off.
[0269] A corner of the document is selected (step 3220). In an
embodiment, the four corners are received as an array of x and y
coordinates CM, where I is equal to the values 1-4 representing the
four corners of the document.
[0270] A determination is made whether the selected corner of the
document is within the mobile document image (step 3225). The x
& y coordinates of the selected corner should be at or between
the edges of the image. According to an embodiment, the
determination whether a corner is within the mobile document image
can be determined using the following criteria: (1) C[I].x>=0
& C[I].x<=Width, where Width=the width of the mobile
document image and C[I].x=the x-coordinate of the selected corner;
and (2) C[I].y>=0 & C[I].y<=Height, where Height=the
height of the mobile document image and C[I].y=the y-coordinate of
the selected corner.
[0271] If the selected corner fails to satisfy the criteria above,
the corner is not within the mobile image and has been cut-off. A
corner cut-off measurement is determined for the corner (step
3230). The corner cut-off measurement represents the relative
distance to the edge of the mobile document image. According to an
embodiment, the corner cut-off measurement can be determined using
the following: [0272] (1) Set H[I] and V[I] to zero, where H[I]
represents the horizontal normalized cut-off measure and V[I]
represents the vertical normalized cut-off measure. [0273] (2) If
C[I].x<0, then set H[I]=-1000*C[I].x/Width [0274] (3) If
C[I].x>Width, set H[I]=1000*(C[I].x-Width)/Width, where Width is
the width of the mobile image [0275] (4) If C[I].y<0, set
V[I]=-1000*C[I].y/Height, where Height is the height of the mobile
image [0276] (5) If C[I].y>Height, set
V[I]=1000*(C[I].y-Height)/Height [0277] (6) Normalize H[I] and V[I]
to fall within the 0-1000 range used by the mobile IQA tests by
setting H[I]=min(1000, H[I]) and V[I]=min (1000, V[I]). [0278] (7)
Set CutOff[I]=min (H(I), V(I)), which is the normalized cut-off
measure of the corner. One can see that the CutOff[I] lies within
[0-1000] range used by the mobile IQA tests and the value increases
as the corner moves away from mobile image boundaries.
[0279] An overall maximum cut-off value is also updated using the
normalized cut-off measure of the corner (step 3235). According to
an embodiment, the following formula can be used to update the
maximum cut-off value: MaxCutOff=max(MaxCutOff, CutOff[I]). Once
the maximum cut-off value is determined, a determination is made
whether more corners are to be tested (step 3225).
[0280] If the selected corner satisfies the criteria above, the
corner is within the mobile document image and is not cut-off. A
determination is then made whether there are additional corners to
be tested (step 3225). If there are more corners to be processed, a
next corner to be test is selected (step 3215). Otherwise, if there
are no more corners to be tested, the test result value for the
test is computing using the maximum test cut-off measurement. In an
embodiment, the test result value V=1000-MaxCutOff. One can see
that V lies within [0-1000] range for the mobile IQA tests and is
equal to 1000 when all the corners are inside the mobile image and
decreases as one or more corner move outside of the mobile
image.
[0281] The test result value is then returned (3245). As described
above, the test result value is provided to the test execution
module 2130 where the test result value can be compared to a
threshold value associated with the test. If the test result value
falls below the threshold associated with the test, detailed test
result messages can be retrieved from the test result message data
store 136 and provided to the user to indicate why the test failed
and what might be done to remedy the test. The user may simply need
to retake the image with the document corners within the frame.
Cut-Side Test
[0282] Depending upon how carefully the user framed a document when
capturing a mobile image, it is possible that one or more sides of
the document can be cut off in the mobile document image. As a
result, important information can be lost from the document. For
example, if the bottom a check is cut off in the mobile image, the
MICR-line might be cut off, rendering the image unusable for a
Mobile Deposit application that uses the MICR information to
electronically deposit checks. Furthermore, if the bottom of a
remittance coupon is cut off in the mobile image, the code line may
be missing, the image may be rendered unusable by a Remittance
Processing application that uses the code information to
electronically process the remittance.
[0283] FIG. 33 illustrates an example of a mobile document image
that features a receipt where one of the ends of the receipt has
been cut off in the image. Unlike the Cut-Corner Test described
above which can be configured to allow a document to pass if the
amount of cut-off falls is small enough that the document image
still receives a test score that meets or exceeds the threshold
associated with the test, the Cut-Side Test is either pass or fail.
If one or more sides of the document subimage are cut off in the
mobile document image, the potential to lose critical information
is too high, and mobile document is marked as failing.
[0284] FIG. 34 is a flow diagram of a method for determining
whether one or more sides of the document are cut off in the
document subimage according to an embodiment. The mobile image is
received (step 3405). In an embodiment, the height and width of the
mobile image can be determined by the preprocessing module 2110.
The corners of the document subimage are then identified in the
mobile document image (step 3410). Various techniques can be used
to identify the corners of the image, including the various
techniques described above. In an embodiment, the preprocessing
module 2110 identifies the corners of the document subimage.
[0285] A side of the document is selected (step 3420). In an
embodiment, the four corners are received as an array of x and y
coordinates C[I], where I is equal to the values 1-4 representing
the four corners of the document.
[0286] A determination is made whether the selected corner of the
document is within the mobile document image (step 3425). According
to an embodiment, the document subimage has four side and each side
S[I] includes two adjacent corners C1[I] and C2[I]. A side is
deemed to be cut-off if the corners comprising the side are on the
edge of the mobile image. In an embodiment, a side of the document
is cut-off if any of the following criteria are met: [0287] (1)
C1[I].x=C2[I].x=0, where x=the x-coordinate of the corner [0288]
(2) C1[I].x=C2[I].x=Width, where Width=the width of the mobile
image [0289] (3) C1[I].y=C2[I].y=0, where y=the y-coordinate of the
corner [0290] (4) C1[I].y=C2[I].y=Height, where Height=the height
of the mobile image
[0291] If the side does not fall within the mobile image, the test
result value is set to zero indicating that the mobile image failed
the test (step 3430), and the test results are returned (step
3445).
[0292] If the side falls within the mobile image, a determination
is made whether there are more sides to be tested (step 3425). If
there are more sides to be tested, an untested side is selected
(step 3415). Otherwise, all of the sides were within the mobile
image, so the test result value for the test is set to 1000
indicating the test passed (step 3440), and the test result value
is returned (step 3445).
Warped Image Test
[0293] The warped image test identifies images where document is
warped. FIG. 35 illustrates an example of a mobile document image
where the document is warped. In some embodiments, the
preprocessing module 2110 can be configured to include de-warping
functionality for correcting warped images. However, in some
embodiments, a Warped Image Test is provided to detect and reject
warped images. One solution for correcting warped images is to
instruct the user to retake the image after flattening the hardcopy
of the document being imaged.
[0294] FIG. 36 is a flow diagram of a method for identifying a
warped image and for scoring the image based on how badly the
document subimage is warped according to an embodiment. A warped
image test score value is returned by the test, and this value can
be compared with a threshold value by the test execution module
2130 to determine whether the image warping is excessive.
[0295] The mobile image is received (step 3605). In an embodiment,
the height and width of the mobile image can be determined by the
preprocessing module 2110. The corners of the document subimage are
then identified in the mobile document image (step 3610). Various
techniques can be used to identify the corners of the image,
including the various techniques described above. In an embodiment,
the preprocessing module 2110 identifies the corners of the
document subimage.
[0296] A side of the document is selected (step 3615). According to
an embodiment, the document subimage has four side and each side
S[I] includes two adjacent corners C1[I] and C2[I].
[0297] A piecewise linear approximation is built for the selected
side (step 3620). According to an embodiment, the piecewise-linear
approximation is built along the selected side by following the
straight line connecting the adjacent corners C1[I] and C2[I] and
detecting position of the highest contrast starting from any
position within [C1[I], C2[I]] segment and moving in orthogonal
direction.
[0298] After the piecewise linear approximation is built along the
[C1[I], C2[I]] segment, the [C1[I], C2[I]] segment is walked to
compute the deviation between the straight line and the
approximation determined using piecewise linear approximation (step
3625). Each time the deviation is calculated, a maximum deviation
value (MaxDev) is updated to reflect the maximum deviation value
identified during the walk along the [C[I], C2[I]] segment.
[0299] The maximum deviation value for the side is then normalized
to generate a normalized maximized deviation value for the selected
size of the document image (step 3630). According to an embodiment,
the normalized value can be determined using the following
formula:
NormMaxDev[I]=1000*MaxDev[I]/Dim, where Dim is the mobile image
dimension perpendicular to side S[I].
[0300] An overall normalized maximum deviation value is then
updated using the normalized deviation value calculated for the
side. According to an embodiment, the overall maximum deviation can
be determined using the formula:
OverallMaxDeviation=max(OverallMaxDeviation, NormMaxDev[I])
[0301] A determination is then made whether there are anymore sides
to be tested (step 3640). If there are more sides to be tested, an
untested side is selected for testing (step 3615). Otherwise, if no
untested sides remain, the warped image test value is computed.
According to an embodiment, the warped image test value can be
determined using the following formula:
V=1000-OverallMaxDeviation
[0302] One can see that V lies within [0-1000] range used by the
image IQA system and is equal to 1000 when the sides S[I] are
straight line segments (and therefore no warp is present). The
computed test result is then returned (step 3650). As described
above, the test result value is provided to the test execution
module 2130 where the test result value can be compared to a
threshold value associated with the test. If the test result value
falls below the threshold associated with the test, detailed test
result messages can be retrieved from the test result message data
store 136 and provided to the user to indicate why the test failed
and what might be done to remedy the test. For example, the user
may simply need to retake the image after flattening out the
hardcopy of the document being imaged in order to reduce
warping.
Image Size Test
[0303] The Image Size Test detects the actual size and the
effective resolution of the document subimage. The perspective
transformation that can be performed by embodiments of the
preprocessing module 2110 allows for a quadrangle of any size to be
transformed into a rectangle to correct for view distortion.
However, a small subimage can cause loss of detail needed to
process the subimage.
[0304] FIG. 37 illustrates an example of a document subimage within
a mobile document image that is relatively small. Small size of the
subimage can cause the loss of important foreground information.
This effect is similar to digital zooming in a digital camera where
image of an object becomes larger, but the image quality of object
can significantly degrade due to loss of resolution and important
details can be lost.
[0305] FIG. 38 is a flow diagram of a process that for performing
an Image Size Test on a subimage according to an embodiment. The
mobile image is received (step 3805). In an embodiment, the height
and width of the mobile image can be determined by the
preprocessing module 2110. The corners of the document subimage are
then identified in the mobile document image (step 3810). Various
techniques can be used to identify the corners of the image,
including the various techniques described above. In an embodiment,
the preprocessing module 2110 identifies the corners of the
document subimage. In the method the corners of the subimage are
denoted as follows: A represents the top-left corner, B represents
the top-right corner of the subimage, C represents the bottom-right
corner of the subimage, and D represents the bottom-left corner of
the subimage.
[0306] A subimage average width is computed (step 3815). In an
embodiment, the subimage average width can be calculated using the
following formula:
Subimage average width as AveWidth=(|AB|+|CD|)/2, where |PQ|
represents the Euclidian distance from point P to point Q.
[0307] A subimage average height is computed (step 3820). In an
embodiment, the subimage average height can be calculated using the
following formula:
AveHeight=(|BC|+|DA|)/2
[0308] The average width and average height values are then
normalized to fit the 0-1000 range used by the mobile IQA tests
(step 3822). The following formulas can be used determine the
normalize the average width and height:
NormAveWidth=1000*AveWidth/Width
NormAveHeight=1000*AveWidth/Height
[0309] A minimum average value is then determined for the subimage
(step 3825). According to an embodiment, the minimum average value
is the smaller of the normalized average width and the normalized
average height values. The minimum average value falls within the
0-1000 range used by the mobile IQA tests. The minimum average
value will equal 1000 if the document subimage fills the entire
mobile image.
[0310] The minimum average value is returned as the test result
(step 3865). As described above, the test result value is provided
to the test execution module 2130 where the test result value can
be compared to a threshold value associated with the test. If the
test result value falls below the threshold associated with the
test, detailed test result messages can be retrieved from the test
result message data store 2136 and provided to the user to indicate
why the test failed and what might be done to remedy the test. For
example, the user may simply need to retake the image by
positioning the camera closer to the document.
MICR-Line Test
[0311] The MICR-line Test is used to determine whether a high
quality image of a check front has been captured using the mobile
device according to an embodiment. The MICR-line Test can be used
in conjunction with a Mobile Deposit application to ensure that
images of checks captures for processing with the Mobile Deposit
information are of a high enough quality to be processed so that
the check can be electronically deposited. Furthermore, if a mobile
image fails the MICR-line Test, the failure may be indicative of
incorrect subimage detections and/or poor overall quality of the
mobile image, and such an image should be rejected anyway.
[0312] FIG. 39A is a flow chart of a method for executing a
MICR-line Test according to an embodiment. A mobile image is
received (step 3905) and a bitonal image is generated from the
mobile image (step 3910). In an embodiment, preprocessor 110
extracts the document subimage from the mobile image as described
above, including preprocessing such as geometric correction. The
extracted subimage can then be converted to a bitonal snippet by
the preprocessor 110. The MICR line is then identified in the
bitonal snippet (step 3915). According to an embodiment, a MICR
recognition engine is then applied to identify the MICR-line and to
compute character-level and overall confidence values for the image
(step 3920). These confidences can then be normalized to the 0-1000
scale used by the mobile IQA tests where 1000 means high quality
and 0 means poor MICR quality. The confidence level is then
returned (step 3925). As described above, the test result value is
provided to the test execution module 2130 where the test result
value can be compared to a threshold value associated with the
test. If the test result value falls below the threshold associated
with the test, detailed test result messages can be retrieved from
the test result message data store 136 and provided to the user to
indicate why the test failed and what might be done to remedy the
test. For example, the user may simply need to retake the image to
adjust for geometrical or other factors, such as poor lighting or a
shadowed document. In some instances, the user may not be able to
correct the errors. For example, if the MICR line on the document
is damaged or incomplete and the document will continue to fail the
test even if the image were retaken.
Code Line Test
[0313] The Code Line Test can be used to determine whether a high
quality image of a remittance coupon front has been captured using
the mobile device according to an embodiment. The Code Line Test
can be used in conjunction with a Remittance Processing application
to ensure that images of remittance coupon captures for processing
with the Remittance Processing information are of a high enough
quality to be processed so that the remittance can be
electronically processed. Furthermore, if a mobile image fails the
Code Line Test, the failure may be indicative of incorrect subimage
detections and/or poor overall quality of the mobile image, and
such an image should be rejected anyway.
[0314] FIG. 39B is a flow chart of a method for executing a Code
Line Test according to an embodiment. A mobile image of a
remittance coupon is received (step 3955) and a bitonal image is
generated from the mobile image (step 3960). In an embodiment,
preprocessor 110 extracts the document subimage from the mobile
image as described above, including preprocessing such as geometric
correction. The extracted subimage can then be converted to a
bitonal snippet by the preprocessor 110. The code line is then
identified in the bitonal snippet (step 3965). According to an
embodiment, a code line recognition engine is then applied to
identify the code line and to compute character-level and overall
confidence values for the image (step 3970). These confidences can
then be normalized to the 0-1000 scale used by the mobile IQA tests
where 1000 means high quality and 0 means poor code line quality.
The confidence level is then returned (step 3975). As described
above, the test result value is provided to the test execution
module 2130 where the test result value can be compared to a
threshold value associated with the test. If the test result value
falls below the threshold associated with the test, detailed test
result messages can be retrieved from the test result message data
store 136 and provided to the user to indicate why the test failed
and what might be done to remedy the test. For example, the user
may simply need to retake the image to adjust for geometrical or
other factors, such as poor lighting or a shadowed document. In
some instances, the user may not be able to correct the errors. For
example, if the code line on the document is damaged or incomplete
and the document will continue to fail the test even if the image
were retaken.
Aspect Ratio Tests
[0315] The width of a remittance coupon is typically significantly
longer than the height of the document. According to an embodiment,
an aspect ratio test can be performed on a document subimage of a
remittance coupon to determine whether the aspect ratio of the
document in the image falls within a predetermined ranges of ratios
of width to height. If the document image falls within the
predetermined ranges of ratios, the image passes the test. An
overall confidence value can be assigned to different ratio values
or ranges of ratio values in order to determine whether the image
should be rejected.
[0316] According to some embodiments, the mobile device can be used
to capture an image of a check in addition to the remittance
coupon. A second aspect ratio test is provided for two-sided
documents, such as checks, where images of both sides of the
document may be captured. According to some embodiments, a
remittance coupon can also be a two-sided document and images of
both sides of the document can be captured. The second aspect ratio
test compares the aspect ratios of images that are purported to be
of the front and back of a document to determine whether the user
has captured images of the front and back of the same document
according to an embodiment. The Aspect Ratio Test could be applied
to various types two-sided or multi-page documents to determine
whether images purported to be of different pages of the document
have the same aspect ratio.
[0317] FIG. 40 illustrates a method for executing an Aspect Ratio
Test for two-sided documents according to an embodiment. In the
embodiment illustrated in FIG. 40, the test is directed to
determining whether the images purported to be of the front and
back side of a document have the same aspect ratio. However, the
method could also be used to test whether two images purported to
be from a multi-page and/or multi-sided document have the same
aspect ratio.
[0318] A front mobile image is received (step 4005) and a rear
mobile image is received (step 4010). The front mobile image is
supposed to be of the front side of a document while the rear
mobile image is supposed to be the back side of a document. If the
images are really of opposite sides of the same document, the
aspect ratio of the document subimages should match. Alternatively,
images of two different pages of the same document may be provided
for testing. If the images are really of pages of the same
document, the aspect ratio of the document subimages should
match.
[0319] The preprocessing module 2110 can process the front mobile
image to generate a front-side snippet (step 4015) and can also
process the back side image to generate a back-side snippet (step
4020).
[0320] The aspect ratio of the front-side snippet is then
calculated (step 4025). In an embodiment, the
AspectRatioFront=Width/Height, where Width=the width of the
front-side snippet and Height=the height of the front-side
snippet.
[0321] The aspect ratio of the back-side snippet is then calculated
(step 4030). In an embodiment, the AspectRatioBack=Width/Height,
where Width=the width of the back-side snippet and Height=the
height of the back-side snippet.
[0322] The relative difference between the aspect ratios of the
front and rear snippets is then determined (step 4035). According
to an embodiment, the relative difference between the aspect ratios
can be determined using the following formula:
RelDiff=1000*abs
(AspectRatioFront-AspectRatioBack)/max(AspectRatioFront,AspectRatioBack)
[0323] A test result value is then calculated based on the relative
difference between the aspect ratios (step 4040). According to an
embodiment, the test value V can be computed using the formula
V=1000-RelDiff:
[0324] The test results are then returned (step 4045). As described
above, the test result value is provided to the test execution
module 2130 where the test result value can be compared to a
threshold value associated with the test. If the test result value
falls below the threshold associated with the test, detailed test
result messages can be retrieved from the test result message data
store 136 and provided to the user to indicate why the test failed
and what might be done to remedy the test. For example, the user
may have mixed up the front and back images from two different
checks having two different aspect ratios. If the document image
fails the test, the user can be prompted to verify that the images
purported to be the front and back of the same document (or images
of pages from the same document) really are from the same
document.
Front-as-Rear Test
[0325] FIG. 41 illustrates a method for performing a front-as-rear
test on a mobile document image. The front-as-rear test can be
adapted to be performed on images purported to be the front and
back side of a check. The Front-as-Rear Test can be used to
determine whether an image that is purported to be the back of a
check is actually an image of the front of the document according
to an embodiment. The Front-as-Rear Test can be used in embodiments
where a user has captured an image of a check to be processed as
payment for a remittance.
[0326] The Front-as-Rear Test is a check specific Boolean test. The
test returns a value of 0 if an image fails the test and a value of
1000 if an image passes the test. According to an embodiment, if a
MICR-line is identified on what is purported to be an image of the
back of the check, the image will fail the test and generate a test
message that indicates that the images of the check have been
rejected because an image of the front of the check was mistakenly
passed as an image of the rear of the check. Similarly, if a code
line is identified on what is purported to be the back of a
remittance coupon, the image will fail the test and generate a test
message that indicates that the images of the remittance coupon
have been rejected because an image of the front of the coupon was
mistakenly passed as an image of the rear of the coupon.
[0327] An image of the rear of the document is received (step 4105)
and the image is converted to a bitonal snippet by preprocessor 110
of the MDIPE 2100 (step 4110). The image may be accompanied by data
indicating whether the image is of a check or of a remittance
coupon. In some embodiments, no identifying information may be
provided, and the testing will be performed to identify either a
code line or an MICR line in the bitonal snippet.
[0328] If the document is identified as a check, a MICR recognition
engine can then be applied to identify a MICR-line in the bitonal
snippet (step 4115). Various techniques for identifying the
MICR-line in an image of a check are described above. The results
from the MICR recognition engine can then be normalized to the
0-1000 scale used by the mobile IQA tests, and the normalized value
compared to a threshold value associated with the test. If the
document is identified as a remittance coupon, a code line
recognition engine can be applied to identify the code line in the
image of the coupon. Various techniques for identifying the code
line in an image of a remittance coupon are described above, such
as identifying text in OCR-A font within the image. If no
information as to whether the image to be tested includes a check
or a remittance coupon is provided, both MICR-line and code line
testing can be performed to see if either a MICR-line or code line
can be found. In an embodiment, the highest normalized value from
the MICR-line and code line tests can be selected for comparison to
the threshold.
[0329] According to an embodiment, the test threshold can be
provided as a parameter to the test along with the with mobile
document image to be tested. According to an embodiment, the
threshold used for this test is lower than the threshold used in
the MICR-line Test described above.
[0330] If the normalized test result equals or exceeds the
threshold, then the image includes an MICR-line or code line and
the test is marked as failed (test result value=0), because a
MICR-line or code line was identified in what was purported to be
an image of the back of the document. If the normalized test result
is less than the threshold, the image did not include a MICR line
and the test is marked as passed (test result value=1000). The test
results value is then returned (step 4125).
Form Identification of Remittance Coupon
[0331] According to an embodiment, the remittance processing step
525 of the method illustrated in FIG. 5 can include a form
identification step. In the form identification step, the mobile
remit server attempts to identify a coupon template that is
associated with a remittance coupon that has been captured in a
mobile image. A coupon template identifies the layout of
information on a remittance coupon. This layout information can be
used improve data capture accuracy because data should be in known
locations on the remittance coupon.
[0332] Form identification can be used in a number of different
situations. For example, form identification can be used for
frequently processed remittance coupons. If the layout of the
coupon is known, capturing the data from known locations on the
coupon can be more accurate than relying on a dynamic data capture
technique to extract the data from the coupon.
[0333] Form identification can also be used for remittance coupons
that lack keywords that can be used to identify key data on the
coupon. For example, if a coupon does not include an "Account
Number" label for an account number field, the dynamic data capture
may misidentify the data in that field. Misidentification can
become even more likely if multiple fields have similar formats.
Form identification can also be used for coupons having ambiguous
data. For example, a remittance coupon might include multiple
fields that include data having a similar format. If a remittance
coupon includes multiple unlabeled fields having similar formats,
dynamic data capture may be more likely to misidentify the data.
However, if the layout of the coupon is known, the template
information can be used to extract data from known positions in the
image of the remittance coupon.
[0334] Form identification can also be used for remittance coupons
having a non-OCR friendly layout. For example, a remittance coupon
may use fonts where identifying keywords and/or form data is
printed using a non-OCR friendly font. Form identification can also
be used to improve the chance of correctly capturing remittance
coupon data when a poor quality image is presented. A poor quality
image of a remittance coupon can make it difficult to locate and/or
read data from the remittance coupon.
[0335] FIG. 42 is a flow chart of a method for processing an image
of a remittance coupon using form identification according to an
embodiment. The method of FIG. 42 can be executed in step 525 of
the method illustrated in FIG. 5. According to an embodiment, the
mobile remittance server can include a remittance processing module
that can be configured to perform step 525 of the method
illustrated in FIG. 5 and the method illustrated in FIG. 42. The
method begins with receiving a binarized document image of a
remittance coupon (step 4205). Various techniques for creating a
bi-tonal subimage from a mobile image are provided above. For
example, step 625 of FIG. 6 describes binarization of a document
subimage. FIG. 10 also illustrates a method binarization of an
image that can be used to generate a bi-tonal image from a mobile
image of a remittance coupon.
[0336] A matching algorithm is executed on the bi-tonal image of
the remittance coupon in an attempt to find a matching remittance
coupon template (step 4210). According to an embodiment, the
remittance server 310 can include a remittance template data store
that can be used to store templates of the layouts of various
remittance coupons. Various matching techniques can be used to
match a template to an image of a coupon. For example, optical
character recognition can be used to identify and read text content
from the image. The types of data identified and the positions of
the data on the remittance coupon can be used to identify a
matching template. According to another embodiment, a remittance
coupon can include a unique symbol or identifier that can be
matched to a particular remittance coupon template. In yet other
embodiments, the image of the remittance coupon can be processed to
identify "landmarks" on the image that may correspond to labels
and/or data. In some embodiments, these landmarks can include, but
are not limited to positions of horizontal and/or vertical lines on
the remittance coupon, the position and/or size of boxes and/or
frames on the remittance coupon, and/or the location of pre-printed
text. The position of these landmarks on the remittance coupon may
be used to identify a template from the plurality of templates in
the template data store. According to some embodiments, a
cross-correlation matching technique can be used to match a
template to an image of a coupon. In some embodiments, the
positions of frames/boxes found on image and/or other such
landmarks, can be cross-correlated with landmark information
associated a template to compute the matching confidence score. If
the confidence score exceeds a predetermined threshold, the
template is considered to be a match and can be selected for use in
extracting information from the mobile image of the remittance
coupon.
[0337] A determination is made whether a matching template has been
found (step 4215). If no matching template is found, a dynamic data
capture can be performed on the image of the remittance coupon
(step 4225). Dynamic data capture is described in detail below and
an example method for dynamic data capture is illustrated in the
flow chart of FIG. 43.
[0338] If a matching template is found, data can be extracted from
the image of the remittance coupon using the template (step 4220).
The template can provide the location of various data, such as the
code line, amount due, account holder name, and account number.
Various OCR techniques can be used to read text content from the
locations specified by the template. Because the location of
various data elements are known, ambiguities regarding the type of
data found can be eliminated. The mobile remittance server 310 can
distinguish between data elements having a similar data type.
Dynamic Data Capture
[0339] FIG. 43 is a flow chart of a dynamic data capture method for
extracting data from an image of a remittance coupon using form
identification according to an embodiment. The method of FIG. 42
can be executed in step 525 of the method illustrated in FIG. 5.
The dynamic data capture method illustrated in FIG. 43 can be used
if a form ID for identifying a particular format of remittance
coupon is not available. The form ID can be provided by a user or
in some instance be read from the image of the remittance coupon.
The method illustrated in FIG. 43 can also be used if the form ID
does not match any of the templates stored in the template data
store. The method begins with receiving a binarized document image
of a remittance coupon (step 4305). Various optical character
recognition techniques can then be used to locate and read fields
from the bitonal image of the remittance coupon (step 4310). Some
example OCR techniques are described below. Once data fields have
been located the data can be extracted from the image of the
remittance coupon (step 4315). In some embodiments, steps 4310 and
4315 can be combined into a single step where the field data is
located and the data extracted in a combined OCR step. Once the
data has been extracted from the image, the data can be analyzed to
identify what data has been extracted (step 4320). The data can
also be analyzed to determine whether any additional data is
required in order to be able to process the remittance coupon.
[0340] According to an embodiment, a keyword-based detection
technique can be used to locate and read the data from the bitonal
image of the remittance coupon in steps 4310 and 4315 of the method
of FIG. 43. The method uses a set of field-specific keywords to
locate fields of interest in the bitonal image. For example, the
keywords "Account Number," "Account #," "Account No.," "Customer
Number," and/or other variations can be used to identify the
account number field on the remittance coupon. According to an
embodiment, text located proximate to the keyword can be associated
with the keyword. For example, text located within a predetermined
distance to the right of or below an "Account Number" keyword could
be identified and extracted from the image using OCR and the text
found in this location can then be treated as the account number.
According to an embodiment, the distance and directions in relation
to the keyword in which the field data can be located can be
configured based on the various parameters, such as locale or
language. The position of the keyword in relation to field that
includes the data associated with the keyword could vary based on
the language being used, e.g. written right to left versus left to
right.
[0341] According to an embodiment, a format-based detection
technique can be used to locate and read the data from the bitonal
image of the remittance coupon in steps 4310 and 4315 of the method
of FIG. 43. For example, an OCR technique can be used to recognize
text in the image of the remittance coupon. A regular expression
mechanism can then be applied to the text extracted from the
bitonal image. A regular expression can be used to formalize the
format description for a particular field, such as "contains 7-12
digits," "may start with 1 or 2 uppercase letters," or "contains
the letter "U" in the second position." According to an embodiment,
multiple regular expressions could be associated with a particular
field, such as an account number, in order to increase the
likelihood of a correct match.
[0342] According to yet another embodiment, a combination of
keyword-based and format-based matching can be used to identify and
extract field data from the bitonal image (steps 4310 and 4315).
This approach can be particularly effective where multiple fields
of the same or similar format are included on the remittance
coupon. A combination of keyword-based and format-based matching
can be used to identify field data can be used to disambiguate the
data extracted from the bitonal image.
[0343] According to an embodiment, a code-line validation technique
can be used to locate and read the data from the bitonal image of
the remittance coupon in steps 4310 and 4315 of the method of FIG.
43. One or more fields may be embedded into the code-line, such as
amount field 215 on the coupon shown in FIG. 2, which appears in
the code line 205. While parsing the code line itself may be
difficult, code-line characters can be cross-checked against fields
recognized in other parts of the remittance coupon. According to an
embodiment, in the event that a particular field is different from
a known corresponding value in the code line, the value in the code
line can be selected over the field value because the code-line
recognition may be more reliable.
[0344] According to an embodiment, a cross-validation technique can
be used where multiple bitonal images of a remittance coupon have
been captured, and one or more OCR techniques are applied the each
of the bitonal images, such as the techniques described above. The
results from the one or more OCR technique from one bitonal image
can be compared to the results of OCR techniques applied one or
more other bitonal images in order to cross-validate the field data
extracted from the images. If conflicting results are found, a set
of results having a higher confidence value can be selected to be
used for remittance processing.
Exemplary Hardware Embodiments
[0345] FIG. 44 is an exemplary embodiment of a mobile device 4400
according to an embodiment. Mobile device 4400 can be used to
implement the mobile device 340 of FIG. 3. Mobile device 4200
includes a processor 4410. The processor 4410 can be a
microprocessor or the like that is configurable to execute program
instructions stored in the memory 4420 and/or the data storage
4440. The memory 4420 is a computer-readable memory that can be
used to store data and or computer program instructions that can be
executed by the processor 4410. According to an embodiment, the
memory 4420 can comprise volatile memory, such as RAM and/or
persistent memory, such as flash memory. The data storage 4440 is a
computer readable storage medium that can be used to store data and
or computer program instructions. The data storage 4440 can be a
hard drive, flash memory, a SD card, and/or other types of data
storage.
[0346] The mobile device 4400 also includes an image capture
component 4430, such as a digital camera. According to some
embodiments, the mobile device 4400 is a mobile phone, a smart
phone, or a PDA, and the image capture component 4430 is an
integrated digital camera that can include various features, such
as auto-focus and/or optical and/or digital zoom. In an embodiment,
the image capture component 4430 can capture image data and store
the data in memory 4220 and/or data storage 4440 of the mobile
device 4400.
[0347] Wireless interface 4450 of the mobile device can be used to
send and/or receive data across a wireless network. For example,
the wireless network can be a wireless LAN, a mobile phone
carrier's network, and/or other types of wireless network.
[0348] I/O interface 4460 can also be included in the mobile device
to allow the mobile device to exchange data with peripherals such
as a personal computer system. For example, the mobile device might
include a USB interface that allows the mobile to be connected to
USB port of a personal computer system in order to transfers
information such as contact information to and from the mobile
device and/or to transfer image data captured by the image capture
component 4430 to the personal computer system.
[0349] As used herein, the term module might describe a given unit
of functionality that can be performed in accordance with one or
more embodiments of the present invention. As used herein, a module
might be implemented utilizing any form of hardware, software, or a
combination thereof. For example, one or more processors,
controllers, ASICs, PLAs, logical components, software routines or
other mechanisms might be implemented to make up a module. In
implementation, the various modules described herein might be
implemented as discrete modules or the functions and features
described can be shared in part or in total among one or more
modules. In other words, as would be apparent to one of ordinary
skill in the art after reading this description, the various
features and functionality described herein may be implemented in
any given application and can be implemented in one or more
separate or shared modules in various combinations and
permutations. Even though various features or elements of
functionality may be individually described or claimed as separate
modules, one of ordinary skill in the art will understand that
these features and functionality can be shared among one or more
common software and hardware elements, and such description shall
not require or imply that separate hardware or software components
are used to implement such features or functionality.
[0350] Where components or modules of processes used in conjunction
with the operations described herein are implemented in whole or in
part using software, in one embodiment, these software elements can
be implemented to operate with a computing or processing module
capable of carrying out the functionality described with respect
thereto. One such example-computing module is shown in FIG. 43.
Various embodiments are described in terms of this
example-computing module 1900. After reading this description, it
will become apparent to a person skilled in the relevant art how to
implement the invention using other computing modules or
architectures.
[0351] Referring now to FIG. 43, computing module 1900 may
represent, for example, computing or processing capabilities found
within desktop, laptop and notebook computers; mainframes,
supercomputers, workstations or servers; or any other type of
special-purpose or general-purpose computing devices as may be
desirable or appropriate for a given application or environment.
Computing module 1900 might also represent computing capabilities
embedded within or otherwise available to a given device. For
example, a computing module might be found in other electronic
devices. Computing module 1900 might include, for example, one or
more processors or processing devices, such as a processor 1904.
Processor 1904 might be implemented using a general-purpose or
special-purpose processing engine such as, for example, a
microprocessor, controller, or other control logic.
[0352] Computing module 1900 might also include one or more memory
modules, referred to as main memory 1908. For example, random
access memory (RAM) or other dynamic memory might be used for
storing information and instructions to be executed by processor
1904. Main memory 1908 might also be used for storing temporary
variables or other intermediate information during execution of
instructions by processor 1904. Computing module 1900 might
likewise include a read only memory ("ROM") or other static storage
device coupled to bus 1902 for storing static information and
instructions for processor 1904.
[0353] The computing module 1900 might also include one or more
various forms of information storage mechanism 1910, which might
include, for example, a media drive 1912 and a storage unit
interface 1920. The media drive 1912 might include a drive or other
mechanism to support fixed or removable storage media 1914. For
example, a hard disk drive, a floppy disk drive, a magnetic tape
drive, an optical disk drive, a CD or DVD drive (R or RW), or other
removable or fixed media drive. Accordingly, storage media 1914
might include, for example, a hard disk, a floppy disk, magnetic
tape, cartridge, optical disk, a CD or DVD, or other fixed or
removable medium that is read by, written to or accessed by media
drive 1912. As these examples illustrate, the storage media 1914
can include a computer usable storage medium having stored therein
particular computer software or data.
[0354] In alternative embodiments, information storage mechanism
1910 might include other similar instrumentalities for allowing
computer programs or other instructions or data to be loaded into
computing module 1900. Such instrumentalities might include, for
example, a fixed or removable storage unit 1922 and an interface
1920. Examples of such storage units 1922 and interfaces 1920 can
include a program cartridge and cartridge interface, a removable
memory (for example, a flash memory or other removable memory
module) and memory slot, a PCMCIA slot and card, and other fixed or
removable storage units 1922 and interfaces 1920 that allow
software and data to be transferred from the storage unit 1922 to
computing module 1900.
[0355] Computing module 1900 might also include a communications
interface 1924. Communications interface 1924 might be used to
allow software and data to be transferred between computing module
1900 and external devices. Examples of communications interface
1924 might include a modem or softmodem, a network interface (such
as an Ethernet, network interface card, WiMedia, IEEE 802.XX or
other interface), a communications port (such as for example, a USB
port, IR port, RS232 port Bluetooth.RTM. interface, or other port),
or other communications interface. Software and data transferred
via communications interface 1924 might typically be carried on
signals, which can be electronic, electromagnetic (which includes
optical) or other signals capable of being exchanged by a given
communications interface 1924. These signals might be provided to
communications interface 1924 via a channel 1928. This channel 1928
might carry signals and might be implemented using a wired or
wireless communication medium. These signals can deliver the
software and data from memory or other storage medium in one
computing system to memory or other storage medium in computing
system 1900. Some examples of a channel might include a phone line,
a cellular link, an RF link, an optical link, a network interface,
a local or wide area network, and other wired or wireless
communications channels.
[0356] Computing module 1900 might also include a communications
interface 1924. Communications interface 1924 might be used to
allow software and data to be transferred between computing module
1900 and external devices. Examples of communications interface
1924 might include a modem or softmodem, a network interface (such
as an Ethernet, network interface card, WiMAX, 802.XX or other
interface), a communications port (such as for example, a USB port,
IR port, RS232 port, Bluetooth interface, or other port), or other
communications interface. Software and data transferred via
communications interface 1924 might typically be carried on
signals, which can be electronic, electromagnetic, optical or other
signals capable of being exchanged by a given communications
interface 1924. These signals might be provided to communications
interface 1924 via a channel 1928. This channel 1928 might carry
signals and might be implemented using a wired or wireless medium.
Some examples of a channel might include a phone line, a cellular
link, an RF link, an optical link, a network interface, a local or
wide area network, and other wired or wireless communications
channels.
[0357] In this document, the terms "computer program medium" and
"computer usable medium" are used to generally refer to physical
storage media such as, for example, memory 1908, storage unit 1920,
and media 1914. These and other various forms of computer program
media or computer usable media may be involved in storing one or
more sequences of one or more instructions to a processing device
for execution. Such instructions embodied on the medium, are
generally referred to as "computer program code" or a "computer
program product" (which may be grouped in the form of computer
programs or other groupings). When executed, such instructions
might enable the computing module 1900 to perform features or
functions of the present invention as discussed herein.
[0358] While various embodiments of the present invention have been
described above, it should be understood that they have been
presented by way of example only, and not of limitation. The
breadth and scope of the present invention should not be limited by
any of the above-described exemplary embodiments. Where this
document refers to technologies that would be apparent or known to
one of ordinary skill in the art, such technologies encompass those
apparent or known to the skilled artisan now or at any time in the
future. In addition, the invention is not restricted to the
illustrated example architectures or configurations, but the
desired features can be implemented using a variety of alternative
architectures and configurations. As will become apparent to one of
ordinary skill in the art after reading this document, the
illustrated embodiments and their various alternatives can be
implemented without confinement to the illustrated example. One of
ordinary skill in the art would also understand how alternative
functional, logical or physical partitioning and configurations
could be utilized to implement the desired features of the present
invention.
[0359] Furthermore, although items, elements or components of the
invention may be described or claimed in the singular, the plural
is contemplated to be within the scope thereof unless limitation to
the singular is explicitly stated. The presence of broadening words
and phrases such as "one or more," "at least," "but not limited to"
or other like phrases in some instances shall not be read to mean
that the narrower case is intended or required in instances where
such broadening phrases may be absent.
* * * * *